{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.3 Matplotlib数据可视化基础"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.3.1 基本图形绘制**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(1) 折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = [1, 2, 3]\n",
    "y = [2, 4, 6]\n",
    "plt.plot(x, y)  # 绘制折线图\n",
    "import pylab as pl\n",
    "pl.xticks(rotation=45)\n",
    "plt.show()  # 展示图形"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PyLab 是一个面向 Matplotlib 的绘图库接口，其语法和 MATLAB 十分相近。PyLab 是一个单独的模块，随 Matplotlib 软件包一起安装"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果想让x和y之间有些数学关系，列表是不太容易进行数学运算的，这时候就可以通过2.1.2小节所讲的Numpy库引入一维数组进行数学运算，代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x1 = np.array([1, 2, 3])\n",
    "\n",
    "# 第一条线：y = x + 1\n",
    "y1 = x1 + 1\n",
    "plt.plot(x1, y1) # 使用默认参数画图\n",
    "\n",
    "# 第二条线：y = x*2\n",
    "y2 = x1*2\n",
    "# color设置颜色，linewidth设置线宽，单位像素，linestyle默认为实线，“--”表示虚线\n",
    "plt.plot(x1, y2, color='red', linewidth=3, linestyle='--')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(2) 柱状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = [1, 2, 3, 4, 5]\n",
    "y = [5, 4, 3, 2, 1]\n",
    "plt.bar(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(3) 散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.random.rand(10)\n",
    "y = np.random.rand(10)\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(4) 直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np  \n",
    "\n",
    "# 随机生成10000个服从正态分布的数据\n",
    "data = np.random.randn(10000)\n",
    "\n",
    "# 绘制直方图，bins为颗粒度，即直方图的长条形数目，edgecolor为长条形边框颜色\n",
    "plt.hist(data, bins=40, edgecolor='black')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**补充知识点：在pandas库中的快捷绘图技巧**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'0'}>]], dtype=object)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3pXVS/zdrFXczM2uPRubgB4CdaR7+XcDDEbFb0nPAmKQ/BL4L3J/G3w98TdIk8BPgxjbkNjOzOuoW+IjYD3ywQvuLwOUV2t8APtGSdGZmNm8+k9VsKapxGKUPocxHQ4dJmllmahxG6UMo8+E9eDOzTLnAm5llygXezCxTLvBmZplygTczy5QLvJlZplzgzcwy5QJvZpYpF3gzs0y5wNs71Lrnqm/MZdZdfKkCe4da91wFn8Zu1k28B29mlikXeDOzTLnAm5llygXezCxTLvBmZplygTczy1TdAi9praR9kp6T9Kyk21L7uZL2SHohPa9M7ZJ0j6RJSfslXdbuN2FmZmdqZA9+GtgWERcBVwC3SLoIuAPYGxEbgb1pHeAaYGN6bAXubXlqMzOrq26Bj4hjEfGdtPwz4CCwBtgM7EzDdgLXp+XNwAMx40mgT9JAq4ObmVltTc3BS1oPfBB4CuiPiGOp6zjQn5bXAIdnbXYktZmZ2SJSRDQ2UOoF/g/wRxHxV5Jejoi+Wf2nImKlpN3Ajoh4IrXvBbZHxMSc19vKzBQO/f39Q2NjYy15Qws1NTVFb29v0TEa0o6spVKJntUbqvafPj5Ztb9aX/8KOPH6/LZtRX8z25azdlquSs5f/gYnp89uS66hoaGq2zZrqX+m2qGcc3R0tBQRw9XGNVTgJZ0F7Aa+ERF/ktq+D4xExLE0BTMeERdK+vO0/NDccdVef3h4OCYmJqp1L6rx8XFGRkaKjtGQdmSVVPdaNNX6q/Vtu2Sauw8sn9e2rehvZtty1k7LVcmtq57nCz96f1tyNbrj14il/plqh3JOSTULfCNH0Qi4HzhYLu7JLmBLWt4CPDar/TPpaJorgFdqFXcz6zDLzqp5RdGBwXVFJ7QGNXI1ySuBTwMHJD2d2v4TsAN4WNLNwCHghtT3OHAtMAm8BtzUysBm1mZvvekrimaiboFPc+nVLgS+qcL4AG5ZYC4zM1sgn8m6xPiGHmZLh2/4scT4hh5mS4f34M3MMuUCb2aWKRd4M7NMucCbmWXKBT5DtY6UMbOlw0fRZKjWkTI+SsZs6fAevJlZplzgzaw5vlZN1/AUjZk1x9eq6Rregzczy5QLvJlZplzgzcwy5QJvZpYpF3gzs0y5wJuZZcoF3swsUy7wXah8rZlSqeTrzZhZVT7RqQuVrzXTs3q64gknPtHEzKCBPXhJX5F0UtIzs9rOlbRH0gvpeWVql6R7JE1K2i/psnaGNzOz6hqZovkqcPWctjuAvRGxEdib1gGuATamx1bg3tbENDOzZtUt8BHxLeAnc5o3AzvT8k7g+lntD8SMJ4E+SQMtympmZk1QRNQfJK0HdkfExWn95YjoS8sCTkVEn6TdwI6IeCL17QW2R8REhdfcysxePv39/UNjY2OteUcLNDU1RW9vb9ExaiqVSvSs3kD/Cjjx+pn9p49P0rN6Q8Vta/UttL9aXzlnp+Wq1D/3d9opuSo5f/kbnJw+u+NynT4+ydDQ0Nvr3fCZKuuWrOWco6OjpYgYrjZuwQU+rZ+KiJXNFPjZhoeHY2Ki5pBFMz4+zsjISNExapLEBdt3s+2Sae4+cOb35IfuvK7mDT/qXQlwvv3V+so5Oy1Xpf65v9NOyVXJraue5ws/en/H5Tp018fgrTffXr/rrru4/fbbAVi9Zi3HjvxD1W2L1g2ff/h5Tkk1C/x8j6I5IWkgIo6lKZiTqf0osHbWuMHUZmZLxZzLCc8+2stHeC2u+R4HvwvYkpa3AI/Nav9MOprmCuCViDi2wIxmZjYPdffgJT0EjADnSToC/D6wA3hY0s3AIeCGNPxx4FpgEngNuKkNmc3MrAF1C3xEfKpK16YKYwO4ZaGhzCxT6XZ/1XT6HH238ZmsZrZ4fLu/ReVr0XSo8vVmfK0ZM5sv78F3qPL1ZirxXo6ZNcJ78GZmmXKBNzPLlAu8mVmmXODNzDLlAm9mlikXeDOzTLnAm1nnSGe6VnoMDK4rOl3X8XHwZtY5apzp6vM/muc9eDOzTLnAt0mtSw1IYvm7V9TsNzNbKE/RtEmtSw1AY3fNMTNbCO/BL4AvCGa2iGp8AesvYSvzHvwC+IJgZovIlxpumvfgzcwy5QJfQ70vSs3MOpmnaGpo5ItSM+sQdW4HuKznbN46/UbFvlxvFdiWAi/pauDzwDLgyxGxox0/x8zsbQ3M0S+178xaPkUjaRnwReAa4CLgU5IuavXPKas3jVLvm/W525dKJU/BmNk71KoznXz0Tjv24C8HJiPiRQBJY8Bm4Lk2/Kz60yh3faxusZ69fc/q6bfXc/1X3czmSNM7d911F6OjoxWHVN37r1Njak0NQXunhxQRrX1B6ePA1RHxG2n908CHIuKzc8ZtBbam1QuB77c0yPydB7xUdIgGdUvWbskJztoO3ZITuidrOecFEbGq2qDCvmSNiPuA+4r6+dVImoiI4aJzNKJbsnZLTnDWduiWnNA9WRvN2Y7DJI8Ca2etD6Y2MzNbRO0o8N8GNkp6n6Qe4EZgVxt+jpmZ1dDyKZqImJb0WeAbzBwm+ZWIeLbVP6eNOm7aqIZuydotOcFZ26FbckL3ZG0oZ8u/ZDUzs87gSxWYmWXKBd7MLFMu8BVI+m+S9kt6WtLfSvpnRWeqRtIfS3o+5X1UUl/RmSqR9AlJz0r6J0kdeRiapKslfV/SpKQ7is5TjaSvSDop6Zmis9Qiaa2kfZKeS3/2txWdqRJJZ0v6e0nfSzn/oOhM9UhaJum7kqqf5YkLfDV/HBG/HBGXAruB3ys4Ty17gIsj4peBHwCfKzhPNc8A/wb4VtFBKlnsS2ws0FeBq4sO0YBpYFtEXARcAdzSob/TfwSuiogPAJcCV0u6othIdd0GHKw3yAW+goj46azVc4CO/SY6Iv42IqbT6pPMnHfQcSLiYER0ytnKlbx9iY2IOA2UL7HRcSLiW8BPis5RT0Qci4jvpOWfMVOQ1hSb6kwxYyqtnpUeHfuZlzQI/Crw5XpjXeCrkPRHkg4Dv0Zn78HP9u+BrxcdokutAQ7PWj9CBxajbiVpPfBB4KmCo1SUpjyeBk4CeyKiI3Mm/wP4HeCf6g1csgVe0v+W9EyFx2aAiPjdiFgLPAh8tvarFZs1jfldZv5L/GAn57SlR1Iv8L+A357zv+OOERFvpSnZQeBySRcXHKkiSdcBJyOi1Mj4JXvDj4j4Vw0OfRB4HPj9NsapqV5WSb8OXAdsigJPbGjid9qJfImNNpB0FjPF/cGI+Kui89QTES9L2sfMdxyd+CX2lcBHJV0LnA28V9L/jIh/V2nwkt2Dr0XSxlmrm4Hni8pST7q5yu8AH42I14rO08V8iY0W08w1dO8HDkbEnxSdpxpJq8pHn0laAXyEDv3MR8TnImIwItYz83f0m9WKO7jAV7MjTS3sB36FmW+sO9WfAe8B9qTDOr9UdKBKJH1M0hHgw8BfS/pG0ZlmS19Uly+xcRB4uFMvsSHpIeDvgAslHZF0c9GZqrgS+DRwVfq7+XTa8+w0A8C+9Hn/NjNz8DUPP+wWvlSBmVmmvAdvZpYpF3gzs0y5wJuZZcoF3swsUy7wZmaZcoE3M8uUC7yZWab+PxN3ebndIUpgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 这种写法只适合pandas中的DataFrame，不能直接用于Numpy的数组\n",
    "import pandas as pd\n",
    "df = pd.DataFrame(data)  # 将绘制直方图中的data数组转换成DataFrame()格式\n",
    "df.hist(bins=40, edgecolor='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 此外，除了写df.hist()外，还可以通过下面这种pandas库里的通用绘图代码绘图：\n",
    "df.plot(kind='hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里是通过设置kind参数为hist来绘制直方图，通过这种通用绘图代码，pandas库除了可以便捷的绘制直方图外，它还可以通过设置kind参数快捷地绘制其他图形，演示代码如下，首先通过2.2.1节的知识点创建一个二维DataFrame表格df。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>人均收入</th>\n",
       "      <th>人均支出</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>8000</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>7000</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广州</th>\n",
       "      <td>6500</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    人均收入  人均支出\n",
       "北京  8000  6000\n",
       "上海  7000  5000\n",
       "广州  6500  4000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame([[8000, 6000], [7000, 5000], [6500, 4000]], columns=['人均收入', '人均支出'], index=['北京', '上海', '广州'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\12424\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 21271 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\12424\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 20140 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\12424\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 19978 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\12424\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 28023 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\12424\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 24191 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\12424\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 24030 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\12424\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 21271 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\12424\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 20140 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\12424\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 19978 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\12424\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 28023 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\12424\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 24191 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\12424\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 24030 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 此时可以通过pandas同时绘制折线图或者柱状图，代码如下：\n",
    "#plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "#plt.rcParams['axes.unicode_minus'] = False  # 解决负号'-'显示为方块的问题\n",
    "\n",
    "df['人均收入'].plot(kind='line')  # kind=line绘制折线图，不设置则默认折线图\n",
    "#df['人均收入'].plot(kind='bar')  # kind=bar绘制柱状图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.3.2 数据可视化常见小技巧**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(1) 添加文字说明"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 通过plt.title(name)给图画添加标题；通过plt.xlable()，plt.ylable()用于添加x轴和y轴标签。\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = [1, 2, 3]\n",
    "y = [2, 4, 6]\n",
    "plt.plot(x, y)\n",
    "plt.title('TITLE')  # 添加标题\n",
    "plt.xlabel('X')  # 添加X轴标签\n",
    "plt.ylabel('Y')  # 添加Y轴标签\n",
    "plt.show()  # 显示图片"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(2) 添加图例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 通过plt.legend()来添加图例，添加前需要设置好lable（标签）参数，代码如下：\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 第一条线, 设定标签lable为y = x + 1\n",
    "x1 = np.array([1, 2, 3])\n",
    "y1 = x1 + 1\n",
    "plt.plot(x1, y1, label='y = x + 1') \n",
    "\n",
    "# 第二条线, 设定标签lable为y = x*2\n",
    "y2 = x1*2\n",
    "plt.plot(x1, y2, color='red', linestyle='--', label='y = x*2')\n",
    "\n",
    "plt.legend(loc='upper left') # 图例位置设置为左上角\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(3) 设置双坐标轴"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面的例子可以在一张图里画出两条线，但如果两条线的取值范围相差比较大，那么画出来的图效果便不太好，那么此时如何来画出两条y坐标轴呢？可以在画完第一个图之后，写如下一行代码即可设置双坐标轴。\n",
    "\n",
    "plt.twinx()\n",
    "\n",
    "需要注意的是如果设置了双坐标轴，那么添加图例的时候，每画一次图就得添加一次，而不能在最后统一添加。这里以y = x和y = x^2为例，演示下如何设置双坐标轴，代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7UTEP32xIYPScVURGlGPCoK50b16rmDpZ/MJhDMWO+YwxJUNqKjz5JHTtCitXQmxsrk1VlXGLYxk2I4amtauw4N7zSnUwCRd2yssYE/6WLnXJHNevh0GDXC6u6Jyz/x5Py+Chj1bz+eo9XNmxAS9e06G05eMKWxZQjDHh7803ITkZPv8c+vfPtVncweOMmLGcDXuP8OhlbRlxQQsrhlWMbAzFGBOevvkG6teHdu3c1VwREf+TzDG7pVsPcM+sFaRn+njzps70Pq1uMXY29GwMxRhjsjt0yJXfvfhieO45N61WrVyDiaoy86ftDJq8jKgqFZg/smeZCybhwk55GWPCxyefuFQpiYnwyCPwxBN5Nk/L8PHkgrW89/Mu+rSty+s3duKUSqU6H1dYs4BijAkPs2a5AfeOHV3hq665JjEHIDH5BHe/u4LlOw4ysndL7r/kNMqXs/GSULIxFGNM6KhCQoK7nyQlxd2geNddrvhVHlbHHWLEjOUcTknn5es6cHmHBsXU4fAVDmMoFlCMMaGxc6cLHhs3umSOVf3bF85bGccjc9dQu1pFJg3uyhkNSkdyx6IKh4Bip7yMMcXL54OJE2HMGHeE8ve/Q6VK+c6W6VNe/NdGJn23lbOb12L8LV2ItuSOYcUCijGm+CQlwcCB8P33cMklLpljs2b5znb4eDqj3lvB95v3M/icpvz18nZUKG8XqYYbCyjGmOJTsyZERcE778CQITkmc8xuc0Iyw2fEEH8ohReuPpMbuzcJfj9NofhTArixiCwWkfUisk5E7vOmPyUi8SKyynvkePuqiPQTkU0iEisijwT6AxhjwtyqVe6ekr17oVw5mD8fbrvNr2CycH0CV43/kaOpmbw3vIcFkyIQkdOy7K9XicgRERktIrVEZKGIbPb+RnntRUTe9Pbdq0WkS37r8OeYMQN4QFXbAT2AkSLSznvvH6rayXt8kcMHKA+MAy4D2gE3ZZnXGFOanTgBjz8O3brB2rWwZYvfs6oqb32zmeEzYmhRpyqf3tuTbs0suWNRqOqmk/troCtwHFei/RHgG1VtDXzjvQa3327tPUYAE/JbR74BRVX3qOoK73kysAFXg9gf3YFYVd2qqmm4usUD/JzXGFNS/fADdOrkqifeeqtL6tizp1+zHkvNYOTsFby68Deu6tyQD+48h/o1Kge3v2XPRcAWVd2B2ydP96ZPBwZ6zwcAM9RZCtQUkfp5LbRAYygi0gxXsH4Z0BMYJSKDgRjcUczBbLM0BHZleR0HnJ3LskfgoiCRkZEF6ZYxJtyMH++OUL76Cvr29Xu2XUnHGT4jht8Sknm8/+kMO7+5JXf0X4SIxGR5PUlVJ+XS9kbgPe95PVXd4z3fC9Tznue0/24I7CEXfgcUEakGzAVGq+oREZkAPAuo9/dVYKi/y8vO++CTwN2HUtjlGGNC5OuvoVEjl8xx7Fh3c2K1an7P/mPsfkbOXkGmT3nn9u5c2KZOEDtbKmWoarf8GolIJHAl8Gj291RVRaTQ+1+/rrsTkQq4YDJLVT/2Vpygqpmq6gP+iTu9lV080DjL60beNGNMaXHwINx+O1x6qTvFBe5KLj+Diaoy7Ydt3Dr1Z6KrVWTBqPMsmATXZcAKVU3wXiecPJXl/U30phd4/+3PVV4CTAE2qOprWaZnPZd2FbA2h9l/AVqLSHMvKt4ILMhvncaYEuLjj90RycyZ8NhjLnVKAaRmZDJm7mqe+nQ9vU+ry7x7zqVZ7ZDe7F0W3MR/T3eB2ycP8Z4PAeZnmT7Yu9qrB3A4y6mxHOWbekVEzgO+B9YAPm/yY16nOuFOeW0H7lTVPSLSAJisqv29+fsDrwPlgamq+nw+H9ZSrxhTErz7rhtw79wZpk51g/AFkHjkBHe+u5yVOw/x5z6tGH1xG8pZcsdC8yf1iohUBXYCLVT1sDctGvgAaALsAK5X1STvYGIs0A93RdjtqhqT85K95VsuL2OM31Td/ST167tkjlOnwogR+SZzzG7VrkPcOTOGIykZvHp9R/qfmefFQ8YP4ZDLywKKMcY/27e74BEbW6BkjtnNXR7Ho/PWULd6Rf45uBun18+9CqPxXzgEFEu9YozJm88H48bBo4+6u9tffBEqF/y+kIxMH3//ciNT/rONc1pEM+6WLtSqarcIlCYWUIwxuUtKgiuugB9/hH79XJbgpk0LvJhDx9MYNXsl/4ndz23nNuPxP51uyR1LIQsoxpjc1awJ9erBjBmummIhbjLctNcld9x7+AQvXdOB689qnP9MpkSyMRRjzP9asQIefNCV5K1ftMHyf63dy/0frKJqxQgmDupK16ZRAeqkyS4cxlDsmNMY46SkuHGS7t1hwwbYtq3Qi/L5lDf+vZm73l1O63rV+XTUeRZMygA75WWMgf/8B+64A377DYYOhVdecXe7F8LR1Awe+GAVX61L4OouDfnbVWdSqUL5AHfYhCMLKMYYN9ielgYLF7raJYW084BL7rg5MZm/Xt6OoT2bWXLHMsTGUIwpq778Eho3hvbtXT6uAiZzzO4HL7mjKoy7uQvnta4dwM6a/NgYijGm+B04AIMHQ//+8MILbloBkjlmp6pM+c82Bk/9mbrVK7JgVE8LJmWUnfIypqxQhY8+glGj3P0lf/2rq6hYBCfSM3l83lrmroijb7t6vHZDJ6pVtN1KWWX/8saUFe++645MunZ1YyUdOhRpcQlHTjBi5nJ+3XWI0Re35s99WltyxzLOAooxpZkq7N4NDRvCddfBsWMwbBhEFO2nv2LnQe6auZyjqRlMHNSVfu1PDVCHTUlmg/LGlFbbtv03mePatYVO5pjdBzG7+Mu8tZxaoxKTBnel7amW3DEchMOgvB2hGFPaZGa6EryPPQbly8NLLxUqmWN2GZk+nv9iA+/8sJ3zWtVm7M2dqVnFkjua/8o3oIhIY2AGrnC94grfvyEiLwNXAGnAFlzxlUM5zL8dSAYy8bPmsTGmkA4cgMsvh6VL3VVcEye6S4OL6OCxNEbOXsGPWw4wtGdzHuvflghL7ljiiEhNYDLQHrc/HwpsAt4HmuGKJV6vqge9AltvAP1xBbZuU9UVeS3fny0iA3hAVdsBPYCRItIOWAi0V9UOwG/kUPA+i96q2smCiTFBFhUFDRq4AfjPPgtIMNm49whXjvsPMTsO8sp1HXniinYWTEquN4B/qWpboCOwAXgE+EZVWwPfeK/B1Z5v7T1GABPyW3i+W4Wq7jkZlVQ12etAQ1X9WlUzvGZLcQXsjTHF7Zdf4MIL3eB7uXIwdy7cckuhMgNn9+WaPVw9/kdS0328P6IH13a1n3lJJSI1gAuAKQCqmuadVRoATPeaTQcGes8HADPUWQrUFJE8s4UW6L8ZItIM6Awsy/bWUODLXGZT4GsRWS4iI/JY9ggRiRGRmIyMjNyaGWNOOn4cHn4YevRwA+87dwZs0T6f8trXm7h71gra1KvOp/eeR+cmltwxzEWc3Id6j+z72+bAPuAdEVkpIpO9GvP1VHWP12YvbngDoCGwK8v8cd603Dvgb09FpBowFxitqkeyTH8cd1psVi6znqeq8SJSF1goIhtV9bvsjVR1EjAJ3FVe/vbLmDJpyRIYPtwFkuHD4eWXoUaNgCw6+UQ6//f+r/x7QwLXdW3EswPbW3LHkiG/MeoIoAtwr6ouE5E3+O/pLQBUVUWk0PtfvwKKiFTABZNZqvpxlum3AZcDF2ku1x+rarz3N1FE5gHdgT8EFGNMAUyZ4krzfvMN9OkTsMVu33+M4TNi2Lr/GE9d0Y4h51pyx1IkDohT1ZNnmD7CBZQEEamvqnu8U1qJ3vvxQNZBuEbetFzle8rLG+mfAmxQ1deyTO8HPAxcqarHc5m3qohUP/kc6AuszW+dxpgcfP65u58E4K23YPXqgAaT737bx5Vj/8O+o6nMHNqd23o2t2BSiqjqXmCXiJzmTboIWA8sAIZ404YA873nC4DB4vQADmc5NZajfG9sFJHzgO+BNYDPm/wY8CZQETjgTVuqqneJSANgsqr2F5EWwDzv/Qhgtqo+n8/nthsbjclq/34YPdpVULz1VleON4BOJnf82xcbaFOvOpNu7UaT6CoBXYcJPn9ubBSRTrjLhiOBrcDtuAOLD4AmwA7cZcNJ3sHEWKAf7rLh21U1Js/l253yxoQpVXj/fbj3Xjh82N2o+NhjEBm4mwlPpGfy2Mdr+HhlPJe1P5VXrutIVUvuWCLZnfLGmNzNnAlDhsBZZ7kxkzPPDOji9xxO4c6Zy1kdd5j7L2nDqN6tLLmjKRILKMaEE58P4uPdDYnXXw8nTrjSvOUDe5XV8h1J3DlzBSlpGUy6tSt9z7Dkjqbo7JSXMeHi5CXA27bBunUBS+aY3fu/7OQvn6ylQc3K/HNwN9rUqx6U9ZjiZae8jDEumePrr7uCVxUqwKuvQpXAD4qnZ/p47rP1TP9pB+e3rs1bN1lyRxNYFlCMCaUDB+Cyy1z6lCuugAkTXO2SQK/maCojZ69g6dYkhp/fnDH9LLmjCTwLKMaEUlQUNGsG998PN9wQkPxb2a3ffYThM2LYdzSVf9zQkas6Wz4uExw2hmJMcfv5Z3jgAZgzJyhHI1l9vnoPD374KzUqV+DtW7vSsXHNoK7PhE44jKHYMa8xxeX4cXjwQTjnHDfwvmtX/vMUks+nvPLVJkbOXkG7Bqew4N6eFkxM0NkpL2OKw+LFrpb71q1w113wwgsBS+aYnUvuuIp/b0jkhm6NeWbgGVSMsOSOJvgsoBhTHN55x9UqWbLE1S4Jkq37jjJi5nK27T/GMwPO4NYeTS0flyk2NoZiTLAsWADNm7s73A8fdpcEB+Fy4JOWbErk3vdWUqF8Ocbd3IVzWkYHbV0m/NgYijGlUWIi3HgjDBjg6pSAO70VpGCiqrz97RaGTvuFRlFVmD+ypwUTExJ2ysuYQFGF2bPhvvsgORmefdZVVAyiE+mZjJm7mvmrdvOnM+vz8nUdqBJpP2sTGrblGRMoM2bAbbe5krxTpkC7dkFd3e5DKYyYGcO63Ud46NLTuKdXSxsvMSFlAcWYovD5IC4OmjRxNyZmZLigEuBkjtn9sj2Ju99dzol0H5MHd+Oi0+vlP5MxQWaD8sYU1ubN/03muH590JI5Zjd72U6eXLCWRlFV+OfgrrSqa8kdTQkZlBeRxiKyWETWi8g6EbnPm15LRBaKyGbvb1Qu8w/x2mwWkSE5tTGmRMnIcIPtHTrAqlXw5JNBvXrrpLQMH3/5ZA2PzVvDuS1r88nInhZMTIGIyHYRWSMiq0QkxpuW477cK/37pojEishqEemS7/L9KAFcH6ivqiu8+vDLgYHAbUCSqr4gIo8AUao6Jtu8tYAYoBug3rxdVfVgXuu0IxQTtvbvd8kcY2Jg4EAYNw4aNAj+ao+mcs+sFfy8LYk7L2zBw5e2pbwVwzJZ+FkCeDvQTVX3Z5n2Ejnsy0WkP3Av0B84G3hDVc/Oa/n5HqGo6h5VXeE9TwY2AA2BAcB0r9l0XJDJ7lJgoaomeUFkIa4+sTElU61a0LIlfPABfPxxsQSTtfGHGTD2B37ddYg3buzEo5edbsHEBFJu+/IBwAx1lgI1vQOMXBXoPhQRaQZ0BpYB9VR1j/fWXiCnUcGGQNaERXHetJyWPUJEYkQkJiMjoyDdMia4fvoJzj3XVVIsV84ldbzuuqBkBs5uwa+7uXbij/hU+eiucxnQKbjJJE2JFnFyH+o9RuTQRoGvRWR5lvdz25f7vf/+vQP+9lREqgFzgdGqeiTr5YmqqiJSpNF9VZ0ETAJ3yqsoyzImII4dg8cfhzffdCV54+ODnh34pEyf8srXm5iwZAtnNYti/C1dqVO9YrGs25RYGaraLZ8256lqvIjUBRaKyMasbxZ1X+7XEYqIVMAFk1mq+rE3OeHk4Y/3NzGHWeOBxlleN/KmGRPe/v1vaN8e3ngD7rkH1q6F7t2LZdVHTqQzbPovTFiyhZu6N2HWsB4WTExAqGq89zcRmAd0J/d9eYH33/5c5SXAFGCDqr6W5a0FwMmrtoYA83OY/Sugr4hEeVcO9PWmGRPe3n0XIiPhu+9g7FioXjxXU23Zd5SB437g+837eW5ge/5+9ZlERliGJFN0IlLVu7AKEamK2x+vJfd9+QJgsHe1Vw/gcJZTYzmvw4+rvM4DvgfWAD5v8mO4cZQPgCbADuB6VU0SkW7AXao6zJt/qNce4HlVfSe/D25XeZmQ+OQTaNHCXQ585IhL5li5crGtfvHGRP783koiI8ox/pYunN3C8nEZ/+V3lZeItMAdlYAb7pitqs+LSDQ578sFGIu7kOo4cLuqxuTZB7ux0ZR5CQlw773w4YcwZAhMm1asq1dVJn67lZe+2sjpp57CpMFdaRQV/PtaTOkSDjc2WuoVU3apulNbo0fD0aPw/PPw0EPF2oWUtEwenruaT3/dzeUd6vPytR2pHGnFsEzJZAHFlF0nkzmee65L5ti2bbGuPv5QCiNmxLB+zxHG9GvLXRe2sOSOpkSzgGLKFp/P1XJv2tTVLPH5YPDgoCdzzG7Z1gPcM2sFaRk+pg45i95t6xbr+o0JBhtDMWXHpk2urvuOHS6ZY7VqIenGu0t38NSCdTSJrsI/B3ejZZ3Q9MOULjaGYkxxSE+HV1+Fp55ySRz/8Y9iywycVVqGj6c+XcfsZTvpfVod3ripM6dUqlDs/TAmWCygmNJt/37o2xdWroRrrnH3lJx6arF3Y19yKvfMWs4v2w9yT6+WPND3NMvHZUodCyimdFJ1ubaio+H0010KlWuuCUlX1sQdZsTMGA4eT+PNmzpzZcfgJ5Q0JhTsFlxT+vzwgyvDGxfngsqsWSELJvNXxXPtxB8pJ8JHd51rwcSUahZQTOlx9Cj8+c9w/vnuZsU9eWaJCKpMn/L3Lzdw35xVdGxUk/mjetK+YY2Q9ceY4mCnvEzp8PXXMGIE7NwJo0bB3/4Wsqu4Dh9P589zVvLtb/sY1KMJT1x+huXjMmWCBRRTOsye7fJuff899OwZsm7EJiYzfMZy4g4e529XncnNZzcJWV+MKW52H4opuebOhVatoGNHl8wxMhIqVQpZd77ZkMB9c1ZRqUI5JgzqylnNaoWsL6bsCYf7UOw43JQ8e/a4QfZrr4XXX3fTTjklZMFEVRm3OJZhM2JoVrsKC0adZ8HElEl2ysuUHKouE/D990NKCrzwAjzwQEi7dDwtg4c+XM3na/YwoFMDXrymA5UqWHJHUzZZQDElx7RpMHSou4pr8mRo0yak3dmVdJwRM5ezce8RHr2sLSMusOSOpmyzgGLCW2amS+bYrBncfLNL4jhoEJQL7dnan7YcYOTsFaRn+njntrPodZoldzTGnxLAU0UkUUTWZpn2vois8h7bRWRVLvNuF5E1Xrs8K30Z8wcbNsAFF7jHsWNQsaLLDBzCYKKqzPhpO4OmLCOqSgXmj+xpwcSUKCJSXkRWishn3uvmIrJMRGK9fXukN72i9zrWe79Zfsv255c5DVcC8neqeoOqdlLVTsBc4OM85u/tte3mx7qMcckcn38eOnWCjRvd8yqhr2CYmpHJox+v4Yn56+jVpg6fjOxJC8sUbEqe+4ANWV6/CPxDVVsBB4E7vOl3AAe96f/w2uUp34Ciqt8BSTm959Ucvh54L7/lGOOXffvgrLPgL3+BgQNdmvlbb3UpVELE51O+WLOHAWN/YM4vuxjVuxX/HNyN6pYp2JQwItII+BMw2XstQB/gI6/JdGCg93yA9xrv/Yskn0HCoo6hnA8kqOrmXN5X4GsRUeBtVZ2U24JEZAQwAiAyMrKI3TIlzslkjrVrw5lnulTzAweGtEsZmT4+W72HsYtjiU08SovaVXn71q5cekbxZys2xg8R2YYWJuWwz30deBio7r2OBg6paob3Og5o6D1vCOwCUNUMETnstd+faweK1H24ibyPTs5T1XgRqQssFJGN3hHPH3gffBK4GxuL2C9Tknz3nbv8d948aNQIZs4MaXfSM33MWxHP+CWxbD9wnDb1qvHmTZ3505n1LeW8CWcZeQ0tiMjlQKKqLheRXsHoQKEDiohEAFcDXXNro6rx3t9EEZkHdAdyDCimDDpyBB59FMaPh+bNYe9eF1BCJDUjkw9j4piwZAvxh1I4o8EpTBzUlb7t6lHOAokp+XoCV4pIf6AScArwBlBTRCK8o5RGQLzXPh5oDMR5+/sawIG8VlCUI5SLgY2qGpfTmyJSFSinqsne877AM0VYnylNvvwS7rzTpZgfPRqeey4kVRQBUtIyee/nnbz93RYSjqTSuUlNnhvYnl6n1bH7SkypoaqPAo8CeEcoD6rqLSLyIXAtMAcYAsz3Zlngvf7Je3+R5pOrK9+AIiLvAb2A2iISBzypqlOAG8l2uktEGgCTVbU/UA+Y5/0gI4DZqvqvfD+1KRs+/BCqV4cff3S1S0LgaGoG7y7dweTvt7L/aBrdm9fi1es60bNVtAUSU5aMAeaIyHPASmCKN30KMFNEYnEXZt2Y34IsOaQpHqouiLRp4y4HTk52yRwrViz2rhw5kc70H7Yz5YdtHDqezvmtazOqdyvObhFd7H0xJlDCITmk3Slvgm/3bhg5Ej75BG6/HaZOdUcnxezgsTSm/rCNaT9uJ/lEBhe1rcuoPq3o3CSq2PtiTGlkAcUEj6oLHg88AKmp8PLLbrykmO1LTmXy91uZuXQHx9My6XfGqYzq08oqKBoTYBZQTPBMmwbDhsGFF7pkjq1aFevq9x4+wdvfbeG9n3eSluHj8g4NGNWnFW3qFf/RkTFlgQUUE1iZmbBjB7Ro4ZI5Vqjg/hZj/q24g8eZsGQLH8bEkanKVZ0bck+vlpYmxZggs0F5Ezjr1sEdd0B8vMvBVcyXAW/ff4zxS2L5eEU8InBt18bc06sljWuFPg+YMcFmg/KmdEhLc8WunnvOVU58881iTeYYm5jM2EWxLPh1NxXKl2NQj6bceWEL6teoXGx9MMZYQDFFtW8fXHQRrFkDN90Eb7wBdeoUy6rX7z7C2MWb+XLtXipFlGfY+S0Ydn5z6lYPXV15Y8oyCyimcLImc+zc2aWYv+KKYln1r7sO8daiWP69IYFqFSO4p1dL7jivBbWqWlJRY0LJxlBMwS1Z4i4F/uQTaNy42FYbsz2JNxfF8t1v+6hRuQJDezbntnObUaOKpZE3xsZQTMly+DCMGQNvv+2u4kpMDHpAUVV+2nKANxdtZunWJGpVjeThfqdxa4+mVo/EmDBjAcX45/PPXTLHPXvc0ckzzwR14F1V+fa3fby1KJblOw5St3pF/vKn07n57CZUibTN1phwZL9M45+5cyEqCj7+GLp3D9pqVJWF6xMYuziW1XGHaVCjEs8OOIPrujWmUoXyQVuvMabobAzF5EwV3n8fTjvNDbonJ7tEjkGqppnpU/61di9vLdrMxr3JNKlVhXt6teTqLo2IjCi+myKNKalsDMWEp7g4uOce+PRTGDoUpkwJWjLHjEwfn67ezdhFsWzZd4wWdary2vUdubJjAyLKWyAxpiSxgGL+y+dzObceegjS0+HVV+G++4KyqrQMH/NWxjF+yRZ2HDjOafWq89ZNnelvZXaNKbH8KbA1FThZi7i9N+0pYDiwz2v2mKp+kcO8/XAlJsvjCm+9EKB+m2CYNs0NvPfpA5MmQcuWAV/FifRMPlwex0SvzG77hqfw9q1dueR0K7NrTDCJSCVcCfaKuH3/R6r6pIg0x1VrjAaWA7eqapqIVARm4Mq8HwBuUNXtea4jvzEUEbkAOArMyBZQjqrqK3nMVx74DbgEiAN+AW5S1fX5fG4bQylOmZmwbZvLBJyW5gbdb7jB3bQYQClpmcz+eSeTvDK7XZrU5N6LWtOrjZXZNSYQ8htDEfdDq6qqR0WkAvAf4D7gfuBjVZ0jIhOBX1V1gojcA3RQ1btE5EbgKlW9Ia8+5HuEoqrfiUizAnyuk7oDsaq61fswc4ABQL4BxRSTNWtcMsfdu2HTJpfM8cZ8q3wWyNHUDGb+5MrsHjiWxtnNa/Ha9Z04t6WV2TWmOHn14I96Lyt4DwX6ADd706cDTwETcPvrp7zpHwFjRUTyqitflDGUUSIyGIgBHlDVg9nebwjsyvI6Djg7t4WJyAhgBEBkkK4kMp7UVPjb39wjKgreeivg95QcTkln+o/bmZqlzO69fVrTvXmtgK7HGPO7CBGJyfJ6kqpOytrAO3O0HGgFjAO2AIdUNcNrEofbd0OWfbiqZojIYdxpsf25dqCQHZ8APIuLbs8CrwJDC7ksALwPPgncKa+iLMvkITHRjZGsWwe33AKvv+7ycQVI0rE0pv5nG9N/3E5yagYXn16XUX1a06lxzYCtwxiTowxV7ZZXA1XNBDqJSE1gHtA2kB0oVEBR1YSTz0Xkn8BnOTSLB7Lm5WjkTTOhcDKZY506cNZZ8OKL8Kc/BWzx2cvsXtbeldk9o4GV2TUm3KjqIRFZDJwD1BSRCO8oJet++uQ+PE5EIoAauMH5XBUqoIhIfVXd4728ClibQ7NfgNbeFQTxwI389zydKU6LFrl0KQsWuNxb77wTsEXvPXyCid+6MrvpmT6u6NiAkb2tzK4x4UZE6gDpXjCpjLtg6kVgMXAt7kqvIcB8b5YF3uufvPcX5TV+Av5dNvwe0AuoLSJxwJNALxHphDvltR2402vbAHd5cH/vnNso4CvcZcNTVXWd35/eFN2hQ+6ekpP13PfvD1gyx11Jx5nw7RY+ionD55XZvdvK7BoTzuoD071xlHLAB6r6mYisB+aIyHPASmCK134KMFNEYoEk3EFBniz1Smm1YAHcfTfs3QsPPghPPQWVi17BcNv+Y4xfHMu8lfGUE+Habo24+0Irs2tMqFnqFRM8Cxa4wfb586FbnuN0ftmckMzYxbF8amV2jTG5sCOU0kIVZs+G00+HLl3g6FGXyLGIl2Cv232YsYti+de6vVSuUJ5BPZpamV1jwpAdoZjA2LUL7roLvvjC3ag4eTJUK9pYxqpdhxi7aDP/3pBI9YoRjOzViqHnNbcyu8aYXFlAKcl8Plc9ccwYl0Ll9ddh1KgiLfKX7Um8+c1mvt+8nxqVK3D/JW0Ycm4zalS26ojGmLxZQCnJpk1zaeYvvtglc2zevFCLyV5mN7pqJGP6teXWc5pSraJtIsYY/9jeoqTJyHDJHFu3hkGD3Kmt664rVDJHVWXJb/t465vNrNh5iLrVK/LXy9txU/fGVmbXGFNgNihfkvz6qxsj2bv3v8kcC8HnUxZuSGDsoljWxB+mYc3K3NWrJdd1bWRldo0poWxQ3vgnNRWeew5eeAFq1YJx4wqVzDHTp3y5dg9jF8X+Xmb3xWvO5KrOVmbXGFN0FlDCXWIi9OoFGzbA4MHw2msQHV2gRWRk+ljw627GLXZldlvWqco/bujIFR2szK4xJnAsoISrrMkczz3XBZJ+/Qq0iLQMHx+vcGV2dyYdp+2p1Rl7c2cua29ldo0xgWdjKOFo4UKXzPGzz6BJkwLPfiI9kw9jdjHx263EH0rhzIY1uLdPKy62MrvGlFo2hmL+18GDLu/W1KnQpg0kJRUooKSkZTJr2Q4mfbeVxORUujaN4vmr2nOhldk1xhQDCyjhYt48d0/Jvn3w6KPwxBNQyb/0JkdTM5jx03amfL+NA8fS6NGiFq/f0IlzrMyuMaYYWUAJF59/Dqee6v526eLXLIdT0pn2gyuzezglnQva1OHePq04q5mV2TXGFD8bQwkVVZg5E9q3dwHk2DGXyLFC/ilOko6lMeU/W5nx4w6vzG49RvVpZWV2jSnDbAylrNqxA+68E776CoYPd2lT/LhJMTH5BJO/38a7S3eQku7K7I7sbWV2jTHhwZ+KjVOBy4FEVW3vTXsZuAJIA7YAt6vqoRzm3Q4kA5lAhqoWvTBHSebzwYQJ8Mgj7gjlzTdh5Mh8Z9tzOIW3v936e5ndK70yu62tzK4xxk8i0hiYAdTDVdudpKpviEgt4H2gGa4C7/WqelDcAOwbQH/gOHCbqq7Icx35nfISkQuAo8CMLAGlL66+cIaIvAigqmNymHc70E1V9/v7oaEUn/KaMgWGDYNLLnFHJc2a5dl8V9Jxxi/ZwkfLd6EKV3dpyN29WtG8dkiPao0xYSi/U14iUh+or6orRKQ6sBwYCNwGJKnqCyLyCBClqmNEpD9wLy6gnA28oapn59WHfI9QVPU7EWmWbdrXWV4uxRWwNzlJT3fJHNu0gVtvhVNOgWuvzTOZ47b9xxjnldktL8L13Rpzl5XZNcYUgaruAfZ4z5NFZAPQEBgA9PKaTQeWAGO86TPUHXUsFZGaIlLfW06OAjGGMhR3uJTjZwC+FhEF3lbVSbktRERGACMAIotYZTBsrFzpkjkmJMBvv7lxkuuuy7X5bwnJjF0Uy2erXZndwec05c4LWnJqDauOaIzJV4SIxGR5PSm3fa53kNAZWAbUyxIk9uJOiYELNruyzBbnTQtOQBGRx4EMYFYuTc5T1XgRqQssFJGNqvpdTg29Dz4J3CmvovQr5E6cgGeegZdecnXdx4/Pc9B9bfx/y+xWiSzP8PNbMOz8FtSpXrEYO22MKeH8GqcWkWrAXGC0qh7Jeq+aqqp3AFAohQ4oInIbbrD+Is1lIEZV472/iSIyD+gO5BhQSo3ERLjgApde/vbb4dVXISoqx6ardh3irW82881GV2b33j6tGNqzOVFWZtcYEwQiUgEXTGap6sfe5ISTp7K8cZZEb3o80DjL7I28abkqVEARkX7Aw8CFqno8lzZVgXLeubqqQF/gmcKsr0TImszxggvcFVx9++bY9OdtSby1yJXZrVmlAg9c0obBVmbXGBNE3lVbU4ANqvpalrcWAEOAF7y/87NMHyUic3CD8ofzGj8B/67yeg83YFMbSACeBB4FKgIHvGZLVfUuEWkATFbV/iLSApjnvR8BzFbV5/P91JTAq7y++srl4PrsM2jaNMcmqsqPWw7w5jebWbYtidrVIhl2fgsG9bAyu8aYovPjKq/zgO+BNYDPm/wYbhzlA6AJsAN32XCSF4DGAv1wlw3frqoxf1hw1nXYnfJFkJQE998P06dD27YwZw507Pg/TVSVJZv28eaizazceYh6p1TkzgtaclP3JlSOtOqIxpjAsDvlS7K5c91Nifv3w+OPw1/+8j/JHH0+5ev1CYxdvJm18UdoWLMyzw1sz7VWZtcYU0pZQCmsr76CBg3gX/+CTp1+n5zpU75Y48rsbkpIpml0FV66pgNXdWlIBauOaIwpxeyUl79UYdo0OPNM6NbNJXOsWBEiXEzOyPQxf9Vuxi2JZeu+Y7SqW41RvVtxeYf6VmbXGBN0dsqrpNi2DUaMgH//2/3t1u33+0rSMnzMXRHHhCxldsfd3IXL2p9q1RGNMWWKBZS8ZGbCuHGu4FW5cu4GxTvvBFyZ3Q9idjFxyRZ2Hz5Bh0Y1+Ovl3biobV0LJMaYMskCSl6mTYP77oPLLoOJE6FJE46nZTB72Xbe/m4r+5JT6dY0ir9f04ELWte26ojGmDLNxlCyS0+HLVvcZcDp6e7ekoEDSU7NYObSHUz+fhtJx9I4p0U0917UinNaWJldY0zohcMYigWUrFasgKFDXfqUzZuhalUOH0/nnR+38c4P2zmcks6FXpndblZm1xgTRsIhoNgpL4CUFHj6aXjlFahbF8aP54BGMOVfG5nx0w6OpmZwSbt6jOrdio5WZtcYY3JkRygJCXD++e6I5I47SHzqb/zz1/28u3QnJzIy6d++PiN7t6Jdg1OKpz/GGFMIdoQSSj6fu3Krbl3o3Zvd/xjP2xmn8t7E5WRk+hjQqSEje7ekVV0rs2uMMf4om0coX3zhkjl+8QW7TqnL+CWxfLQ87vcyu/f0akUzK7NrjClB7AiluO3fD//3f/Duu2w96wLGfbWNT3asp7wIN5zlyuw2irIyu8YYUxhlJ6B88AGMGsWmctUZ++h0PtdoIuNOMOScZtx5YQvqnWJldo0xpijKTEBZu+QXxl7+EP+q246qEeUZfk5Thp1nZXaNMSZQSm9AUYWpU1nZ6HTe2lOBRaf0onrdCP58bjNutzK7xhgTcH6lwRWRqSKSKCJrs0yrJSILRWSz9zfHwukiMsRrs1lEhgSq43naupVlA4cwaOEerlp8kBU7D/Jg3zb8Z0wf7u97mgUTY0yZVJB9uThvikisiKwWkS75Lt+fq7xE5ALgKDBDVdt7014CklT1BRF5BIhS1THZ5qsFxADdAAWWA11V9WBe6yvsVV6akcEPr0zhzXVH+LlhO2qXy2R433YMOqcZVa3MrjGmFPPnKq+C7MtFpD9wL9AfV1P+DVU9O6/l+7WXVdXvRKRZtskDcLXmAaYDS4Ax2dpcCixU1SSv4wtx9Ynf82e9BXHkRDqD//4Zq1IbcWrdozx5fgNuvKSDldk1xhhPAfflA3CBR4GlIlJTROqr6p7cll+U/7bXy7LgvUC9HNo0BHZleR3nTfsDERkBjACIjCz4KanqFSNodlpTrk2O47qh11Cxgh2RGGPKlAgRicnyepKqTvJjvtz25bntv4MSUH6nqioiRbpD0vvgk8Cd8iro/CLC6zd3AfI9zWeMMaVRhqp2K8oCirovL0pt2gQRqQ/g/U3MoU080DjL60beNGOMMeEht315gfffRQkoC4CTV20NAebn0OYroK+IRHlXDvT1phljjAkPue3LFwCDvau9egCH8xo/Af8vG34P+Ak4TUTiROQO4AXgEhHZDFzsvUZEuonIZABvMP5Z4Bfv8czJAXpjjDHFqyD7cuALYCsQC/wTuCff5ZfJ5JDGGFPKhENyyKKc8jLGGGN+ZwHFGGNMQFhAMcYYExAWUIwxxgREWA7Ki4gPSCnk7BFARgC7EyjWr4KxfhWM9atgSmO/KqtqSA8SwjKgFIWIxBT1btFgsH4VjPWrYKxfBWP9Cg475WWMMSYgLKAYY4wJiNIYUPzJrhkK1q+CsX4VjPWrYKxfQVDqxlCMMcaERmk8QjHGGBMCFlCMMcYERIkJKCIyVUQSRWRtlmm1RGShiGz2/kblMu8Qr81mERmSU5sA9+tlEdkoIqtFZJ6I1Mxl3u0iskZEVmWrtBasfj0lIvHe+lZ5NaNzmrefiGwSkVivxnSw+/V+lj5tF5FVucwbzO+rsYgsFpH1IrJORO7zpod0G8ujXyHdxvLoV0i3sTz6FdJtTEQqicjPIvKr16+nvenNRWSZ9z28LyI5lqsVkUe9NptE5NJA9SvgVLVEPIALcOUY12aZ9hLwiPf8EeDFHOarhUvBXAuI8p5HBblffYEI7/mLOfXLe287ULsYv6+ngAfzma88sAVoAUQCvwLtgtmvbO+/CjwRgu+rPtDFe14d+A1oF+ptLI9+hXQby6NfId3GcutXqLcxQIBq3vMKwDKgB/ABcKM3fSJwdw7ztvO+o4pAc++7Kx/oPgbiUWKOUFT1OyB7LZUBwHTv+XRgYA6zXgosVNUkVT0ILAT6BbNfqvq1qp6823UprtJZscrl+/JHdyBWVbeqahowB/c9B71fIiLA9cB7gVqfv1R1j6qu8J4nAxtw9bNDuo3l1q9Qb2N5fF/+CNo2ll+/QrWNqXPUe1nBeyjQB/jIm57b9jUAmKOqqaq6DVefpHtwe1w4JSag5KKe/reC2F6gXg5tGgK7sryOw/8NPxCGAl/m8p4CX4vIchEZUUz9GeWdJpmay+mbUH5f5wMJqro5l/eL5fsSkWZAZ9z/IsNmG8vWr6xCuo3l0K+w2MZy+b5Cto2JSHnvVFsi7j8dW4BDWf5jkNv3EOp9mN9KekD5nbpjw7C6BlpEHsfl5ZmVS5PzVLULcBkwUkQuCHKXJgAtgU7AHtyhfzi5ibz/5xj070tEqgFzgdGqeiTre6HcxnLrV6i3sRz6FRbbWB7/jiHbxlQ1U1U74Y4muwNtA7XscFHSA0qCiNQH8P4m5tAmHmic5XUjb1pQichtwOXALd6O6A9UNd77mwjMI8iHsaqa4G3UPlxJz5zWF6rvKwK4Gng/tzbB/r5EpAJuJzRLVT/2Jod8G8ulXyHfxnLqVzhsY3l8XyHfxrxlHwIWA+cANb1+Qe7fQ0h+k4VR0gPKAuDkFTVDgPk5tPkK6CsiUd7hd19vWtCISD/gYeBKVT2eS5uqIlL95HOvX2tzahvAftXP8vKqXNb3C9Dau/okErgR9z0H28XARlWNy+nNYH9f3rn1KcAGVX0ty1sh3cZy61eot7E8+hXSbSyPf0cI4TYmInXEuxJPRCoDl+DGdxYD13rNctu+FgA3ikhFEWkOtAZ+DkS/Ai7UVwX4+8Adpu4B0nHnEO8AooFvgM3Av4FaXttuwOQs8w7FDWTFArcXQ79icec8V3mPiV7bBsAX3vMWuCs3fgXWAY8XQ79mAmuA1biNtH72fnmv++OujtlSHP3ypk8D7srWtji/r/Nwp7NWZ/l36x/qbSyPfoV0G8ujXyHdxnLrV6i3MaADsNLr11q8q8y8df7s/Xt+CFT0pl8JPJNl/se972oTcFkgt/1APiz1ijHGmIAo6ae8jDHGhAkLKMYYYwLCAooxxpiAsIBijDEmICygGGOMCQgLKMYYYwLCAooxxpiA+H+Kw/fRLm+IlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 第一条线, 设定标签lable为y = x\n",
    "x1 = np.array([10, 20, 30])\n",
    "y1 = x1\n",
    "plt.plot(x1, y1, color='red', linestyle='--', label='y = x')\n",
    "plt.legend(loc='upper left')  # 该图图例设置在左上角\n",
    "\n",
    "plt.twinx()  # 设置双坐标轴\n",
    "\n",
    "# 第二条线, 设定标签lable为y = x^2\n",
    "y2 = x1*x1\n",
    "plt.plot(x1, y2, label='y = x^2') \n",
    "plt.legend(loc='upper right')  # 改图图例设置在右上角\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(4) 设置图片大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S7pu9iDcXbuaMri144rv9aNUoOehYEoVUxiIix2HB+l2Mzchkc0ER91zYg1vPPJm4OM0dluOjMhYROQbl5c6zH3/D4++toG3jZKbfOpQBKU2DjiVRTmUsIhKmbbuL+Mm0bD5dtZ2L+7blN5f3oXHdhKBjSQxQGYuIhOGfy7dx17Rs9haX8rsr+nDNaR10SkupMipjEZEjKC4t5/fvLuP/fbKGnm0aMmX0ELq1bhh0LIkxKmMRkcNYu30vY6dksnBDAT8Y0pH7Lz6F5IT4oGNJDFIZi4gcwquZG7l/9iLi44xnvz+QC3u3CTqSxDCVsYjIQfYeKOXB13KYuWADp3VqypOjUmnXpG7QsSTGqYxFREIWbyxgbEYma3bsZey53Rh7TlfqxOuUllL9VMYiUuu5Oy9+tpbfvb2MpvUTmHzzEIae3DzoWFKLqIxFpFbbubeY8dOzeX/ZNs47pRWPXtWPZvUTg44ltYzKWERqrS++2cG4qZns2lvCQ9/pxfWnd9LcYQmEylhEap3SsnImvr+Spz9cRefm9XnhutPo3a5x0LGkFlMZi0itsjF/P3dkZDJv3S6uGtieX1x6KvWT9FIowdJfoIjUGu8s3sw9MxZS7jBxVH9G9m8XdCQRQGUsIrVAUUkZv3pzCX//cj192zdm0uhUOjavH3Qskf9QGYtITFuxtZD0yZks31rImDO7cPf5PUiso7nDEllUxiISk9ydKf/K5Rev51A/sQ5/veE0zu7RKuhYIoekMhaRmFOwv4T7Zi3izUWbOaNrC564ph+tGiYHHUvksFTGIhJT5q/bxdiMTLbuLuKnF/bkljO7EBenucMS2VTGIhITysudZz76hifmrqBt42Sm3TqUASlNg44lEpawy9jM4oF5wEZ3v6TSbdcDvwc2hhY97e7PV1VIEZEj2ba7iDunZfHZqh1c0rctv7miD42SE4KOJRK2Y3lnfAewFGh0mNunuvvtJx5JRCR8Hy7fxt3TstlbXMojV/bhu2kddEpLiTphlbGZtQcuBn4N/KRaE4mIhKG4tJxH31nG85+uoWebhky9dghdWzUMOpbIcQn3nfGTwD3Akf7SrzSzM4EVwJ3unnuC2UREDmnt9r2kZ2SyaGMBPxzakfsuOoXkhPigY4kct6POfDezS4Bt7j7/CKu9DnRy977AXOClw9zXGDObZ2bz8vLyjiuwiNRuszM3cPFTn7B+5z6e/f5AJozsrSKWqGfufuQVzH4L/AAoBZKp+Mx4lrt//zDrxwM73f2I/wVKWlqaz5s377hCi0jts/dAKQ+8tphZCzZyWqemPDkqlXZN6gYdS+SYmNl8d0+rvPyou6nd/WfAz0J3cjZwd+UiNrO27r45dPVSKg70EhGpEos3FpCekcm6HXsZe243xp7TlTrxOqWlxI7jnmdsZhOAee4+BxhrZpdS8e55J3B91cQTkdrM3fnLZ2t55O1lNKufyOQfDWFIl+ZBxxKpckfdTV1dtJtaRI5kx54DjJ+xkA+WbeO8U1rx+6v60bR+YtCxRE7Ice+mFhGpaZ9/s51xU7LI31fCw9/pxXWnd9LcYYlpKmMRiRilZeVMfH8lT3+4is4t6vPiDadx6klHPBZUJCaojEUkImzYtY87pmQxf90urh7Ynl+MPJV6iXqJktpBf+kiEri3F23mpzMXUu4wcVR/RvZvF3QkkRqlMhaRwBSVlDHhjSVM/mo9/do35qnRqXRsXj/oWCI1TmUsIoFYsbWQ9MmZLN9ayC1nduGu83uQWEdzh6V2UhmLSI1ydzK+zmXCGzk0SKrDSzcO4qzuLYOOJRIolbGI1JiC/SX8bNZC3lq0hW91a8Hj3+1Hq4bJQccSCZzKWERqxPx1uxibkcnW3UXcO6InY77Vhbg4zR0WAZWxiFSzsnLn2Y++4Ym5KzipSTLTbx1KakrToGOJRBSVsYhUm627i7hzahaff7ODS/q25TdX9KFRckLQsUQijspYRKrFh8u2cdf0bPYVl/LIlX34bloHndJS5DBUxiJSpQ6UlvHoO8t54dM19GzTkKevHULXVg2DjiUS0VTGIlJl1mzfS3rGAhZv3M0Ph3bkvotOITkhPuhYIhFPZSwiVWLWgg088Opi6sTH8ecfDOSCU9sEHUkkaqiMReSE7DlQyoOvLmZW5kYGdWrGk6P6c1KTukHHEokqKmMROW6LNxaQnpHJuh17uePcbqSf05U68TqlpcixUhmLyDFzd/7y2Vp+9/ZSmtdPYvKPhjCkS/OgY4lELZWxiByTHXsOcPf0bD5cnsd5p7Tm91f1pWn9xKBjiUQ1lbGIhO3zVdsZNzWL/H0l/OLSU/nh0I6aOyxSBVTGInJUpWXl/OEfK/jTP7+hc4v6/PWGQfQ6qVHQsURihspYRI5ow6593DEli/nrdvHdtPY8fOmp1EvUS4dIVdIWJSKH9faizfx05kLKHSaO6s/I/u2CjiQSk1TGIvI/ikrKmPDGEiZ/tZ5+HZowaVQqKc3rBR1LJGapjEXkvyzfUkh6xgJWbN3DLWd14a5v9yCxjuYOi1QnlbGIABVzhyd/vZ4Jry+hYXIdXr5xEGd2bxl0LJFaQWUsIhTsK+HeWQt5e/EWvtWtBU98tz8tGyYFHUuk1lAZi9Ry89ftZGxGFlt3F3HviJ6M+VYX4uI0d1ikJoVdxmYWD8wDNrr7JZVuSwJeBgYCO4Br3H1tFeYUkSpWVu48889V/OEfKzmpSTLTbx1KakrToGOJ1ErH8s74DmApcKiZ/jcBu9y9q5mNAh4BrqmCfCJSDbbuLmLclCy+WL2D7/Q7iV9f3ptGyQlBxxKptcI6RNLM2gMXA88fZpWRwEuhyzOAc03nyBOJSB8s28qIiZ+QlZvPo1f25alR/VXEIgEL953xk8A9QMPD3N4OyAVw91IzKwCaA9tPNKCIVI0DpWU88vZy/vLZGnq2acjT1w6ga6sGQccSEcIoYzO7BNjm7vPN7OwTeTAzGwOMAUhJSTmRuxKRY7A6bw9jp2SyeONurhvakZ9ddArJCfFBxxKRkHDeGQ8DLjWzi4BkoJGZ/d3dv3/QOhuBDsAGM6sDNKbiQK7/4u7PAc8BpKWl+YmGF5Gjmzl/Aw+8tpjEOnE894OBnH9qm6AjiUglRy1jd/8Z8DOA0DvjuysVMcAc4DrgC+Aq4AN3V9mKBGjPgVIeeHUxszM3MqhzMyaO6k/bxnWDjiUih3Dc84zNbAIwz93nAC8AfzOzVcBOYFQV5ROR47BoQwHpGQtYv3Mf487rRvo53YjX3GGRiHVMZezu/wT+Gbr84EHLi4CrqzKYiBy78nLnL5+t4ZF3ltGiQRIZPxrC4C7Ng44lIkehM3CJxIgdew5w9/RsPlyex7d7tebRK/vStH5i0LFEJAwqY5EY8Pmq7YybmkX+/hImjDyVHwzpiKb6i0QPlbFIFCspK+fJf6zgT//8hi4t6vPXGwbR66RDnSRPRCKZylgkSuXu3McdUzJZsD6fa9I68NClvaiXqE1aJBppyxWJQm8u3My9sxaCw1OjU7m030lBRxKRE6AyFoki+4vLmPDGEjK+Xk+/Dk2YNCqVlOb1go4lIidIZSwSJZZvKSQ9YwErtu7hlrO6cPf5PUiID+v/ehGRCKcyFolw7s4rX63nl28soWFyAi/fOIgzu7cMOpaIVCGVsUgEK9hXwr2zFvL24i18q1sLnvhuf1o2TAo6lohUMZWxSISat3Ynd0zJYuvuIn42oic/+lYX4nRKS5GYpDIWiTBl5c6fPlzFk++vpF2Tusy47XT6d2gSdCwRqUYqY5EIsqWgiDunZvHF6h1c2u8kfn15bxomJwQdS0SqmcpYJEK8v3Qrd0/PpqiknEev6svVA9vrlJYitYTKWCRgB0rL+N3by3jxs7Wc0rYRk0an0rVVg6BjiUgNUhmLBGh13h7SMzLJ2bSb60/vxL0jepKcEB90LBGpYSpjkYDMnL+BB15bTGKdOP7fD9P4dq/WQUcSkYCojEVq2J4DpTzw6mJmZ25kUOdmTBzVn7aN6wYdS0QCpDIWqUGLNhSQnrGA9Tv3ced53bn9nK7Ea+6wSK2nMhapAeXlzl8+W8Mj7yyjRYMkpowZyqDOzYKOJSIRQmUsUs227znA3dOz+efyPM7v1ZpHr+pLk3qJQccSkQiiMhapRp+t2s64qVkU7C/hlyNP5ftDOmrusIj8D5WxSDUoKSvnD3NX8MxH33Byywa8fOMgTmnbKOhYIhKhVMYiVSx35z7GTskkc30+16R14KFLe1EvUZuaiByeXiFEqtCbCzdz76yF4DBpdCrf6XdS0JFEJAqojEWqwP7iMia8kUPG17n079CESaNT6dCsXtCxRCRKqIxFTtCyLbtJn5zJym17uPWsk7nr/O4kxMcFHUtEoojKWOQ4uTt//2o9v3pjCQ2TE/jbTYP4VreWQccSkSikMhY5DgX7SvjpzIW8k7OFM7u35PGr+9GyYVLQsUQkSh21jM0sGfgYSAqtP8PdH6q0zvXA74GNoUVPu/vzVRtVJDL8a+1O7sjIZFvhAe67qCc3n9GFOJ3SUkROQDjvjA8A57j7HjNLAD41s7fd/ctK601199urPqJIZCgrd/704Sr+8I8VtG9aj5m3nU6/Dk2CjiUiMeCoZezuDuwJXU0IfXl1hhKJNFsKihg3NZMvV+9kZP+T+NVlvWmYnBB0LBGJEWF9Zmxm8cB8oCvwR3f/6hCrXWlmZwIrgDvdPbfqYooE5/2lW7l7ejZFJeX8/qq+XDWwvU5pKSJVKqz5F+5e5u79gfbAIDPrXWmV14FO7t4XmAu8dKj7MbMxZjbPzObl5eWdQGyR6negtIxfvJ7DTS/No23jurwx9gyuTuugIhaRKmcVe6GP4QfMHgT2uftjh7k9Htjp7o2PdD9paWk+b968Y3pskZqyOm8P6RmZ5GzazfWnd+LeET1JTogPOpaIRDkzm+/uaZWXh3M0dUugxN3zzawu8G3gkUrrtHX3zaGrlwJLqyCzSI1zd2Yu2MiDry0mqU4cz/8wjfN6tQ46lojEuHA+M24LvBR6xxsHTHP3N8xsAjDP3ecAY83sUqAU2AlcX12BRarLngOl/Hz2Il7N2sTgzs2YOCqVNo2Tg44lIrXAMe+mriraTS2RZOGGfNIzMsnduY9x53Xnx8O7Eq+5wyJSxY57N7VILCsvd174dA2PvruMlg2SmHrLUE7r1CzoWCJSy6iMpdbavucAd03L5qMVeZzfqzWPXtWXJvUSg44lIrWQylhqpU9XbufOaVkU7C/hlyNP5ftDOmrKkogERmUstUpJWTlPzF3Bsx99w8ktG/DyjYM4pW2joGOJSC2nMpZaI3fnPsZOySRzfT6jTuvAg9/pRb1EbQIiEjy9Ekmt8MbCTfxs5iIAJo1O5Tv9Tgo4kYjI/09lLDFtf3EZE97IIePrXPp3aMKk0al0aFYv6FgiIv9FZSwxa9mW3dw+OZNv8vZw29kn85NvdychPqzTsYuI1CiVscQcd+fvX63nl28soXHdBF6+cRDf6tYy6FgiIoelMpaYkr+vmJ/OXMi7OVs5q3tLHv9uP1o0SAo6lojIEamMJWb8a+1O7sjIJG/PAe6/6BRuOqMzcTqlpYhEAZWxRL2ycuePH67iyX+soEOzesy87XT6tm8SdCwRkbCpjCWqbSkoYtzUTL5cvZOR/U/iV5f1pmFyQtCxRESOicpYotY/lmxl/IxsDpSW89jV/bhyQDud0lJEopLKWKLOgdIyfvvWMv76+Vp6tW3EpGtTObllg6BjiYgcN5WxRJVv8vaQPjmTJZt3c/3pnfjZRT1JqhMfdCwRkROiMpao4O7MmL+Bh+bkkFQnjud/mMZ5vVoHHUtEpEqojCXiFRaV8PNXF/Na1iYGd27GxFGptGmcHHQsEZEqozKWiJadm8/YKZnk7tzHT77dnR8P70q85g6LSIxRGUtEKi93nv90NY++s5xWDZOYestQTuvULOhYIiLVQmUsESev8AB3T8/moxV5XHBqax65si9N6iUGHUtEpNqojCWifLIyjzunZrO7qIRfXtab7w9O0dxhEYl5KmOJCCVl5TwxdwXPfvQNJ7dswN9vHkTPNo2CjiUiUiNUxhK43J37SM/IJCs3n9GDOvDgJadSN1Fzh0Wk9lAZS6Bez97EfbMWgcHT16ZySd+Tgo4kIlLjVMYSiP3FZfzi9Rym/CuX1JQmPDUqlQ7N6gUdS0QkECpjqXFLN+8mPSOTb/L28H9nn8yd3+5OQnxc0LFERAKjMpYa4+78/ct1/PLNpTSum8DfbxrMsK4tgo4lIhK4o5axmSUDHwNJofVnuPtDldZJAl4GBgI7gGvcfW2Vp5Wolb+vmJ/OXMi7OVs5u0dLHru6Hy0aJAUdS0QkIoTzzvgAcI677zGzBOBTM3vb3b88aJ2bgF3u3tXMRgGPANdUQ16JQl+v2cm4KZnk7TnAzy8+hRuHdSZOp7QUEfmPo5axuzuwJ3Q1IfTllVYbCTwcujwDeNrMLPSzUkuVlTtPf7CKie+voEOzesy87XT6tm8SdCwRkYgT1mfGZhYPzAe6An90968qrdIOyAVw91IzKwCaA9urMKtEkc0F+xk3JYuv1uzksv4n8cvLetMwOSHoWCIiESmsMnb3MqC/mTUBZptZb3dffKwPZmZjgDEAKSkpx/rjEiXmLtnK+BnZFJeW89jV/bhyQDud0lJE5AiOaT6Ju+cDHwIXVrppI9ABwMzqAI2pOJCr8s8/5+5p7p7WsmXL4woskauopIyH5+Two5fn0a5JXd5IP4OrBrZXEYuIHEU4R1O3BErcPd/M6gLfpuIArYPNAa4DvgCuAj7Q58W1yzd5e0ifnMmSzbu5YVgn7h3Rk6Q6OqWliEg4wtlN3RZ4KfS5cRwwzd3fMLMJwDx3nwO8APzNzFYBO4FR1ZZYIoq7M33+Bh56LYfkhDheuC6Nc09pHXQsEZGoEs7R1AuB1EMsf/Cgy0XA1VUbTSJdYVEJ989ezJzsTQzp0ownr0mlTePkoGOJiEQdnYFLjkt2bj7pGZlszN/PXd/uzv8N70q85g6LiBwXlbEck/Jy5/lPV/PoO8tp3SiZqWOGkNapWdCxRESimspYwpZXeIC7pmfz8Yo8Ljy1DY9c2ZfG9TR3WETkRKmMJSyfrMzjzqnZFBaV8KvLevO9wSmasiQiUkVUxnJEJWXlPP7eCp796Bu6tWrAKzcPpkebhkHHEhGJKSpjOazcnftIz8gkKzef0YNSePCSXtRN1NxhEZGqpjKWQ3o9exP3zVoEBn+8dgAX920bdCQRkZilMpb/sq+4lF/MWcLUebkMSGnCxFGpdGhWL+hYIiIxTWUs/7F0825un7yA1dv38uPhJzPuvO4kxB/T6ctFROQ4qIwFd+dvX67jV28upXHdBP5+02CGdW0RdCwRkVpDZVzL5e8r5p4ZC3lvyVbO7tGSx67uR4sGSUHHEhGpVVTGtdjXa3Zyx5RMtu85wM8vPoUbh3UmTqe0FBGpcSrjWqis3Jn0wUqeen8lKc3qMeu2YfRp3zjoWCIitZbKuJbZXLCfO6Zk8fWanVye2o5fXtabBkn6MxARCZJehWuRuUu2Mn5GNsWl5Tx+dT+uHNg+6EgiIoLKuFYoKinjd28v46+fr+XUkxoxaXQqXVo2CDqWiIiEqIxj3Kpte0jPyGTp5t3cOKwzPx3Rg6Q6OqWliEgkURnHKHdn+vwNPPRaDnUT4/nL9Wmc07N10LFEROQQVMYxaHdRCT+fvZg52ZsY2qU5T47qT+tGyUHHEhGRw1AZx5is3HzSMxawKb+Iu8/vzm1ndyVec4dFRCKayjhGlJc7/++T1fz+3eW0bpTMtFuGMLBjs6BjiYhIGFTGMSCv8AB3Tc/m4xV5jOjdht9d0ZfG9RKCjiUiImFSGUe5j1fk8ZNp2RQWlfDry3tz7aAUzLRbWkQkmqiMo1RJWTmPvbecP3+0mm6tGvDKzYPp0aZh0LFEROQ4qIyj0Pod+0ifkkl2bj7XDk7hgYt7UTdRc4dFRKKVyjjKzMnexP2zFoHBn743gIv6tA06koiInCCVcZTYV1zKw3NymDZvAwNSmjBxVCodmtULOpaIiFQBlXEUWLJpN+kZC1i9fS8/Hn4y487rTkJ8XNCxRESkihz1Fd3MOpjZh2a2xMxyzOyOQ6xztpkVmFlW6OvB6olbu7g7L32+lsv+9BmFRaW8ctNgxl/QU0UsIhJjwnlnXArc5e4LzKwhMN/M5rr7kkrrfeLul1R9xNpp195i7pm5kLlLtjK8R0seu7ofzRskBR1LRESqwVHL2N03A5tDlwvNbCnQDqhcxlJFvlq9g3FTs9i+5wA/v/gUbhzWmTid0lJEJGYd02fGZtYJSAW+OsTNQ80sG9gE3O3uOScer3YpK3cmfbCSp95fSUqzesy6bRh92jcOOpaIiFSzsMvYzBoAM4Fx7r670s0LgI7uvsfMLgJeBbod4j7GAGMAUlJSjjdzTNqUv59xU7P4es1Orkhtx4TLetMgScfXiYjUBubuR1/JLAF4A3jX3Z8IY/21QJq7bz/cOmlpaT5v3rxjiBq73svZwj0zF1JcWs6vLuvNFQPaBx1JRESqgZnNd/e0ysuP+tbLKk50/AKw9HBFbGZtgK3u7mY2iIqjtHecYOaYV1RSxm/fWspLX6yjd7tGTBo9gM4t6gcdS0REalg4+0GHAT8AFplZVmjZfUAKgLs/C1wF3GZmpcB+YJSH85a7Flu1rZDbJ2eybEshN53RmXsu7EFSHZ3SUkSkNgrnaOpPgSMeyuvuTwNPV1WoWObuTJ+3gYfm5FA3MZ4Xrz+N4T1bBR1LREQCpCOEatDuohLun72Y17M3MbRLc54c1Z/WjZKDjiUiIgFTGdeQrNx80jMWsCm/iPEX9ODWs04mXnOHRUQElXG1Ky93nvtkNY+9u5zWjZKZdssQBnZsFnQsERGJICrjarStsIi7pmXzycrtjOjdht9d0ZfG9RKCjiUiIhFGZVxNPl6Rx0+mZVFYVMpvLu/D6EEdqJglJiIi8t9UxlWsuLScx99bzp8/Xk331g145eYh9GjTMOhYIiISwVTGVWj9jn2kZywge0MB1w5O4YGLe1E3UXOHRUTkyFTGVeS1rI3cP3sxcQbPfG8AI/q0DTqSiIhECZXxCdpXXMpDr+Uwff4GBnZsysRR/WnftF7QsUREJIqojE9AzqYC0jMyWbN9L7cP78q487pRJz4u6FgiIhJlVMbHwd15+Yt1/PrNpTSpl8ArNw3m9K4tgo4lIiJRSmV8jHbtLWb8jIX8Y+lWhvdoyWNX96N5g6SgY4mISBRTGR+DL1fvYNyULHbsPcADl/TixmGdNHdYREROmMo4DKVl5Uz6YBWTPlhJx+b1mX3dMHq3axx0LBERiREq46PYlL+fcVOy+HrtTq4Y0I4JI3vTIEm/NhERqTpqlSN4L2cL42cspLSsnD9c04/LU9sHHUlERGKQyvgQikrK+M1bS3n5i3X0bteISaMH0LlF/aBjiYhIjFIZV7JqWyG3T85k2ZZCbj6jM+Mv7EFSHZ3SUkREqo/KOMTdmTYvl4fnLKFuYjwvXn8aw3u2CjqWiIjUAipjYHdRCffNWsQbCzdz+snN+cM1/WndKDnoWCIiUkvU+jLOXL+LsVMy2ZRfxPgLenDrWScTH6e5wyIiUnNqbRmXlzt//ng1j7+3nNaNkpl2yxAGdmwWdCwREamFamUZbyss4q5p2XyycjsX9WnDb6/oS+O6CUHHEhGRWqrWlfFHK/K4a1oWhUWl/ObyPowe1EGntBQRkUDVmjIuLi3nsfeW89zHq+neugGTfzSE7q0bBh1LRESkdpTxuh17GZuRSfaGAr43OIUHLulFcoLmDouISGSI+TJ+LWsj989eTJzBM98bwIg+bYOOJCIi8l9itoz3Hijl4Tk5TJ+/gYEdmzJxVH/aN60XdCwREZH/EZNlnLOpgPSMTNZs30v6OV2549xu1ImPCzqWiIjIIR21ocysg5l9aGZLzCzHzO44xDpmZk+Z2SozW2hmA6on7pG5O3/9bA2X//Fz9h4o5ZWbB3PX+T1UxCIiEtHCeWdcCtzl7gvMrCEw38zmuvuSg9YZAXQLfQ0Gngl9rzG79hYzfsZC/rF0K+f2bMXvr+5Hs/qJNRlBRETkuBy1jN19M7A5dLnQzJYC7YCDy3gk8LK7O/ClmTUxs7ahn6122bn53PK3+ezcW8yDl/TihmGdNHdYRESixjF9ZmxmnYBU4KtKN7UDcg+6viG07L/K2MzGAGMAUlJSjjHq4TWrn0jrxsk8f10avds1rrL7FRERqQlhf5hqZg2AmcA4d999PA/m7s+5e5q7p7Vs2fJ47uKQOjSrx6v/d7qKWEREolJYZWxmCVQU8SvuPusQq2wEOhx0vX1oWY3RbmkREYlW4RxNbcALwFJ3f+Iwq80Bfhg6qnoIUFBTnxeLiIhEu3A+Mx4G/ABYZGZZoWX3ASkA7v4s8BZwEbAK2AfcUOVJRUREYlQ4R1N/ChxxH3DoKOofV1UoERGR2kRnwxAREQmYylhERCRgKmMREZGAqYxFREQCpjIWEREJmMpYREQkYCpjERGRgKmMRUREAqYyFhERCZhVnDwrgAc2ywPWVeFdtgC2V+H9BUljiUyxMpZYGQdoLJEqVsZSHePo6O7/898WBlbGVc3M5rl7WtA5qoLGEpliZSyxMg7QWCJVrIylJseh3dQiIiIBUxmLiIgELJbK+LmgA1QhjSUyxcpYYmUcoLFEqlgZS42NI2Y+MxYREYlWsfTOWEREJCpFRRmb2V/MbJuZLT7M7WZmT5nZKjNbaGYDDrrtOjNbGfq6ruZSHzLn0cbxvVD+RWb2uZn1O+i2taHlWWY2r+ZSH1oYYznbzApCebPM7MGDbrvQzJaHnq97ay71oYUxlvEHjWOxmZWZWbPQbRHzvJhZBzP70MyWmFmOmd1xiHWiZVsJZywRv72EOY6o2FbCHEu0bCvJZva1mWWHxvKLQ6yTZGZTQ7/7r8ys00G3/Sy0fLmZXVAlodw94r+AM4EBwOLD3H4R8DZgwBDgq9DyZsDq0PemoctNI3gcp/87HzDi3+MIXV8LtAj6uTiGsZwNvHGI5fHAN0AXIBHIBnpF8lgqrfsd4INIfF6AtsCA0OWGwIrKv9so2lbCGUvEby9hjiMqtpVwxlJp/UjeVgxoELqcAHwFDKm0zv8Bz4YujwKmhi73Cj0XSUDn0HMUf6KZouKdsbt/DOw8wiojgZe9wpdAEzNrC1wAzHX3ne6+C5gLXFj9iQ/taONw989DOQG+BNrXSLDjEMZzcjiDgFXuvtrdi4EpVDx/gTnGsYwGMqoxznFz983uviB0uRBYCrSrtFq0bCtHHUs0bC9hPieHE1HbynGMJZK3FXf3PaGrCaGvygdQjQReCl2eAZxrZhZaPsXdD7j7GmAVFc/VCYmKMg5DOyD3oOsbQssOtzwa3ETFO5h/c+A9M5tvZmMCynSshoZ2A71tZqeGlkXtc2Jm9agoqJkHLY7I5yW0Sy2Vin/xHyzqtpUjjOVgEb+9HGUcUbWtHO05iYZtxczizSwL2EbFP0QPu624eylQADSnmp6XOid6B1L1zGw4FS8uZxy0+Ax332hmrYC5ZrYs9I4uUi2g4rRve8zsIuBVoFuwkU7Yd4DP3P3gd9ER97yYWQMqXgTHufvuILOcqHDGEg3by1HGEVXbSph/XxG/rbh7GdDfzJoAs82st7sf8riRmhAr74w3Ah0Out4+tOxwyyOWmfUFngdGuvuOfy93942h79uA2VTBbpHq5O67/70byN3fAhLMrAVR+JwcZBSVdrtF2vNiZglUvFC+4u6zDrFK1GwrYYwlKraXo40jmraVcJ6TkIjfVv7N3fOBD/nfj2X+8/s3szpAY2AH1fS8xEoZzwF+GDpSdAhQ4O6bgXeB882sqZk1Bc4PLYtIZpYCzAJ+4O4rDlpe38wa/vsyFeMI7F9w4TCzNqHPVzCzQVT8re0A/gV0M7POZpZIxUY7J7ik4TGzxsBZwGsHLYuo5yX0+34BWOruTxxmtajYVsIZSzRsL2GOIyq2lTD/vqJlW2kZekeMmdUFvg0sq7TaHODfswquouJgNA8tHxU62rozFXsxvj7RTFGxm9rMMqg44rCFmW0AHqLiA3fc/VngLSqOEl0F7ANuCN2208x+ScUfNcCESrtNalQY43iQis8k/hTaNku94iTlranYjQIVz9lkd3+nxgdwkDDGchVwm5mVAvuBUaE/5FIzu52KF/p44C/unhPAEP4jjLEAXA685+57D/rRSHtehgE/ABaFPgsDuA9IgejaVghvLNGwvYQzjmjZVsIZC0THttIWeMnM4qn4x880d3/DzCYA89x9DhX/8Pibma2i4gDPUQDunmNm04AlQCnw49Au7xOiM3CJiIgELFZ2U4uIiEQtlbGIiEjAVMYiIiIBUxmLiIgETGUsIiISMJWxiIhIwFTGIiIiAVMZi4iIBOz/A2wXzVcjNl/oAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (8, 6)\n",
    "x = [1, 2, 3]\n",
    "y = [2, 4, 6]\n",
    "plt.plot(x, y)\n",
    "plt.show()  # 显示图片"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(4) 设置X轴角度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = [1, 2, 3]\n",
    "y = [2, 4, 6]\n",
    "plt.plot(x, y)  # 绘制折线图\n",
    "\n",
    "import pylab as pl\n",
    "pl.xticks(rotation=45)  # 设置角度为45度\n",
    "\n",
    "plt.show()  # 展示图形"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(6) 中文显示问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\12424\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 20013 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\12424\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 25991 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\12424\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 26631 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\12424\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 39064 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\12424\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 36724 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\12424\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 20013 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\12424\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 25991 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\12424\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 36724 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\12424\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 26631 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\12424\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 39064 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "#plt.rcParams['axes.unicode_minus'] = False  # 解决负号'-'显示为方块的问题\n",
    "\n",
    "x = [1, 2, 3]\n",
    "y = [2, 4, 6]\n",
    "plt.plot(x, y)\n",
    "plt.title('中文标题')  # 添加标题\n",
    "plt.xlabel('中文X轴')  # 添加X轴标签\n",
    "plt.ylabel('中文Y轴')  # 添加Y轴标签\n",
    "plt.show()  # 显示图片"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(7) 绘制多图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如下图所示，有时我们需要在一张画布上输出多个图形，在Matplotlib库中有当前的图形（figure）以及当前轴（axes）概念，其对应的就是当前画布以及当前子图，在一张画布（figure）上可以绘制多个子图（axes）。绘制多图通常采用subplot()函数或subplots()函数，"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先来讲解subplot()函数，如下图所示，它通常含有三个参数，子图的行数、列数以及第几个子图，例如subplot(221)表示的就是绘制2行2列的子图（共4个子图），并在第1个子图上进行绘图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3., 0., 0., 0., 0., 1., 0., 0., 0., 1.]),\n",
       " array([2. , 2.2, 2.4, 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 演示代码如下：\n",
    "import matplotlib.pyplot as plt\n",
    "# 绘制第一个子图：折线图\n",
    "ax1 = plt.subplot(221)  \n",
    "plt.plot([1, 2, 3], [2, 4, 6])  # 这里plt其实也可以换成ax1\n",
    "\n",
    "# 绘制第二个子图：柱状图\n",
    "ax2 = plt.subplot(222)  \n",
    "plt.bar([1, 2, 3], [2, 4, 6])\n",
    "\n",
    "# 绘制第三个子图：散点图\n",
    "ax3 = plt.subplot(223)  \n",
    "plt.scatter([1, 3, 5], [2, 4, 6])\n",
    "\n",
    "# 绘制第四个子图：直方图\n",
    "ax4 = plt.subplot(224)  \n",
    "plt.hist([2, 2, 2, 3, 4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了加强大家对画布（figure）和子图（axes）的理解，我们通过下面的代码来做一个简单演示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1cb635bbf60>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GkjGPlS7nDkUhiqTVm4vX8PC0ZVz640P48WF7B12ONFBKzdjM2prZ7Dq+1tTMlpvZ5OhHp/SWKLnghXkr+fUzikIUSaeKjVu58al5dGzbkht1pSmrpXqZ+m6grtmEnYHH3H1oekqSXDN58Rp+9cQcin7QWlGIImni7tz09DzWb6pi/MBidm2m9xJns3p/KprZqcBGYHUdS7oCZ5pZqZmNNbOEDd7MhphZmZmVlZeXN7xiySqxKMQO++zBmAHHKwpRJE2enPUZr773Oded1pEj27UKuhzZSUmbsZk1B34DDEuybCbQw92LgWbAGYkW6X2J+Sc+CvHhQcXsuZs2loikw/IvN3HLc+9RckgbBp/UPuhyJA3qu0w9DBjl7uuSbLaZ5+5fR2+XAR3SVZxkL0UhijSO6hrn2klz+J4Z91xQqP0XOaK+y9Q9gKvMbDLQxczGJFgzwcwKowMCegNz01yjZJmV6zbTb2wpoChEkXS7/62PKYtO2Tqg9e5BlyNpkvTM2N27xW5HG/JIM7vN3YfHLbsVeBQw4Dl3f6MxCpXs8GU0CrFyc5WiEEXSbN5n67j39Q/4qaZs5ZyUh364e/fozeG1/nwBkR3Vkucqt1QxYFwkCnHCoBJFIYqk0fZTtvQ+/VyjCVySFluqqhk8voxFqxSFKNIY4qds7bV786DLkTRTM5adFolCfJeZSyv4s6IQRdJusqZs5TxNX5CdEotC/L9Fa7it99GcrShEkbSq2LiVGzRlK+fpzFgaLD4K8cZeh3NxyQ+CLkkkp7g7v356Pus2bdWUrRynM2NpsFgU4uUnt+e/uh8WdDkiOefJWZ/xynurue60wzVlK8epGUuDxKIQLyo+iGG9jgi6HJGcEz9l6zJN2cp5asaywx6Pi0K8TW+xEEk7TdnKP3rNWHbIi/NWcZOiEEUaVWzK1r19CjVlK0/ozFhSNnnxGn75xGxFIYo0ovmfrdeUrTykn6aSkjJFIYo0usiUrdmaspWHdJla6vXeyvUMVBSiSKO74+X3+bh8IxMHacpWvtGZsSS1pHwDAx4spaWiEEUaVfyUrRM7aMpWvlEzljrFohBrHCYMUhSiSGPRlC3RZWpJqHYU4mH7KApRpDFoypaAzowlgfgoxDEDihSFKNKIntKULUHNWGqJj0K8v+9xlLT/ftAlieSs5V9u4vfPvUexpmzlPV2mlm8pClEAzOxKoE/0072AGe5+eYAl5aT4KVsjNWUr76V8Zmxmbc1sdpKvjzWzaWY2PD2lSSbV1DjXP6koRAF3v8/du7t7d2Aq8EDAJeWk2JStW3sfpSlbskOXqe8GEm6nNbPzgCbufgLQ3sw6pKM4yQx353fPvce/5igKUbYxs/2Btu5eFnQtuUZTtqS2lJqxmZ0KbARW17GkOzApevs14MQE9zHEzMrMrKy8vLwBpUpjufu1xUyYvozLuykKUbZzFXBfoi/oeG44TdmSROptxmbWHPgNMCzJshbAiujtCqBt7QXuPtrdi9y9qKCgoCG1SiMYPeVj/v7mx1xUfCDDTlcUokSY2feAU4DJib6u47nh/hidsnX3+YWasiXfSuXMeBgwyt3XJVmzgW2XsFumeL8SsMdLl3P7S4v4aef9uK13J/2GLvFOIrJxy4MuJJdMXryG8dOWMfDHB2vKlmwnlabZA7jKzCYDXcxsTII1s9h2aboQWJqW6qTRxKIQT+5YwL2KQpTv+gkwJegickn8lK2hvXQVSrZX71ub3L1b7Ha0IY80s9vcPX7X9LPAVDNrB5wOdE13oZI+sSjE4w5qzf19FYUo3+Xuvw66hlyiKVtSnx36KRx9u8PCWo0Yd68ksolrOnCKu69PX4mSTvFRiGMvURSiSCZoypbUJ21DP9x9Ldt2VEsILVxZycCHZrLfnrsx/lJFIYpkwqcVm7jl+YWasiVJ6fpknvjki430f3AGLXdpysTBJRTsoShEkcZWXeP86ok5GGjKliSlcZh5YNX6zfQdM0NRiCIZFpuydW+fQk3ZkqR0ZpzjvtzwNX3HzGD95ioevrRYUYgiGaIpW7Ij1Ixz2Fdbqrhk3Ew+W7uZsYpCFMkYTdmSHaXL1DlqS1U1g8aX8f6qSkb3VxSiSCbFpmxNHFSiKVuSEjXjHBQfhfinPl049YjvTCcVkUby1gflmrIlO0yXqXNMfBTiH845mnP0WpVIxqzduJUbnpxLh300ZUt2jM6Mc0h8FOINPzmcvl0VhSiSKe7OTU/PZ+2mrYwbeLymbMkO0ZlxDrnntQ/iohAPDbockbwSP2XrqHbaLCk7Rs04RzwwZQl/e/Ojb6MQtXtTJHM0ZUt2lppxDnhi5nJGvPS+ohBFAqApW5IOes04y700fxU3Pa0oRJGgxKZsjbxAU7ak4XRmnMXe+qCcax6fzbEHtea+vscqClEkwxasiE7Z6rQf5x6jdy5Iw+mnd5aatayCKybM4rBoFOLuzXWRQySTNm+t5prHZ/P9ls0Zca6mbMnOSakZm1kbM+tpZnoHewgsXFnJJeNmsu+eu/KwohBFAhGbsnXP+V00ZUt2Wr3N2MxaAy8AxcCbZlaQYE1TM1tuZpOjH50aoVYhFoVYSstdmjJhULGiEEUCoClbkm6pXNvsDFzr7tOjjflY4NUEax5z96HpLlC22RaF6EwY1FWbRUQCoClb0hjqPTN297eijbgbkbPjaQmWdQXONLNSMxtrZnoBM80qNm6l39hS1m+uYvxARSGKBMHd+fUzkSlbf7qwi6ZsSdqk+pqxAX2AtUBVgiUzgR7uXgw0A85IcB9DzKzMzMrKy8t3ouT889WWKgY8WMqnFZsYO6CITgdouo9IEP757gpeXrCaa3tqypakV0rN2COuAuYBZydYMs/dV0VvlwEdEtzHaHcvcveigoLvvOwsddhSVc3gaBTifX2PVRSiSEA+rdjE7597j+JD2jCkm6ZsSXqlsoFrqJn1j366F7AuwbIJZlZoZk2A3sDcNNaYt6qqa7jqkXcpXVrBPRcUKgpRJCCasiWNLZUz49FAPzObAjQBPjOz22qtuRWYAMwBprn7G+ktM//EohD/vWgNtyoKUSRQsSlbt5xzlDZOSqOod6OVu68Fetb64+G11iwgsqNa0qB2FGI/RSGKBEZTtiQTNIErhGJRiEMUhSgBMrNRZnZW0HUEaUtVNb98Yo6mbEmj01uQQiYWhXjh8Qdyk6IQJSBmdhKwr7s/H3QtQfrjy4v4aM0GJgwq1pQtaVQ6Mw6RSTM/jUQhdtqPEecqClGCYWbNgAeApWZ2TtD1BGXKB+U89J+lDPzxwZzUQe8AkcalZhwSL81fxbCn59GtYwH39lEUogSqP7AQuAsoNrOray/I9bkBazdu5XpN2ZIMUjMOgSlxUYj3KwpRgncMMNrdVwMTgVNqL8jluQGasiVB0E/9gM1aVsHlikKUcPkIiE21KAKWBVhLxmnKlgRBP/kD9P6qSgaOm0nbVrsoClHCZCzwoJldSGS87c8Dridjvp2ydbCmbElmqRkH5JMvNtJvbCm7N2/KxMElikKU0HD3r4Dzg64j06prnGsnRaZs3aMpW5JhasYBiI9CnDi4RBN9RELgH1M+ZubStYy8oJAD2+iYlMzSa8YZ9t0oxD2CLkkk72nKlgRNzTiDvtpSxSXjIlGIYxSFKBIKsSlbbVpoypYER5epM2RLVTWXPVzGwpWV/KPfcXRVFKJIKGjKloSBmnEGVFXX8N+PvsuMTyr4U58u/L8fKgpRJAxiU7Yu+ZGmbEmwdJm6kdXUODc+NY833lcUokiYxE/ZGna6pmxJsHRm3Ijcnd8//x7PzF6hKESREHF3bn42MmVr3MDjNWVLAqcz40Y08vUPeHjaMi476RBFIYqEyNPvruCl+ZqyJeGRUjM2szZm1tPM9m7sgnLFmKlL+Ov/fUSfogP59Rk/1A5NkZD4tGITv9OULQmZepuxmbUGXgCKgTfNLOEuBzMba2bTzGx4mmvMOpNmfsptL77PGZ325fbzFIUoEhaxKVugKVsSLqmcGXcGrnX3EcCrwLG1F5jZeUATdz8BaG9mHdJbZvZ4ORqFeFKHvRWFKBIysSlbt55zlKZsSajU24zd/S13n25m3YicHU9LsKw7MCl6+zXgxNoLcj3/FGDqh+Vc8/gcjjmoNf/odxy7NNWmEJGw0JQtCbNUXzM2oA+wFqhKsKQFsCJ6uwL4zhtpczn/FGDWsrUMeXgW7Qta8OAARSGKhImmbEnYpdSMPeIqYB5wdoIlG4Ddordbpnq/uSIShVhK21a7MGFQCXvurihEkTCJTdm6+/xCTdmSUEplA9dQM+sf/XQvYF2CZbPYdmm6EFialuqywFJFIYqEmqZsSTZI5Qx2NNDPzKYATYDPzOy2Wmueja4ZCVwAvJjeMsNp9fotXDxmBtU1NUwcXKwoRJGQ0ZQtyRb1vrDp7muBnrX+eHitNZVm1j267i53X5+2CkOqYuNW+o6dwfrNVTx6WYmiEEVCJn7K1oOXaMqWhFvadhlFm/akehfmgFgU4vKKTYwfWEznA/YKuiQRqSU2ZWtoryM4en9N2ZJwy6uNVukQi0J8b2Ulo35xLCccqihEkbDRlC3JNmrGOyA+CvGe8wvpcaSiEEXCprrGuW7SXEBTtiR76M2wKYqPQvzDOUfRW0MDRELpH1M+pnRp5BdmTdmSbKEz4xS4O7dEoxCvP60j/U44OOiSRCSB2JStMzrty3nH6hdmyR5qxim49/UPGB+NQrzqlMOCLkdEEthuylZvBbRIdtFl6nqMmbqEvygKUST0YlO2JgwqpnULTdmS7KIz4yRiUYinH60oRJEwm/qhpmxJdlMzrkN8FOKfLlQUouQPM2tqZsvNbHL0o1PQNSWjKVuSC3SZOoFYFGKXA/dSFKLko87AY+4+NOhC6hObslWxcStjB2jKlmQvnRnXEh+FOO6SYkUhSj7qCpxpZqVmNtbMQnsQxKZsXdvzcE3ZkqymZhxn0eptUYgPDypWFKLkq5lAD3cvBpoBZ9ReYGZDzKzMzMrKy8szXiBoypbkFjXjqPgoxAmDSthnj12DLkkkKPPcfVX0dhnQofYCdx/t7kXuXlRQkPkNU5qyJblGzZhIFGLfsTP4pjoShaipPZLnJphZoZk1AXoDc4MuqLbRU5ZQurSCW84+Sser5ITQvhaUKWs3bqXf2Bms26QoRJGoW4FHAQOec/c3Aq5nOwtWrGfk64s1ZUtySl434w1ff8Ml40pZpihEkW+5+wIiO6pDJzZlq/XumrIluSVvm/GWqmouG1/GgpWV/KPvcYpCFMkCsSlbD1+qKVuSW+ptxma2J/A40ATYCPRx96211jQFlkQ/AK529/lprjVtIlGIs5m25Evu7aMoRJFsED9lq1tHTdmS3JLKBq6LgZHufhqwGuiVYE1sSED36EdoG/G2KMTPufWcozj3mAOCLklE6rFuk6ZsSW6rtxm7+yh3fz36aQGwJsGyeocEhOF9ie7OrS8s5JnZK7iuZ0f6KwpRJPTcnZufWUDFxq3c26eLpmxJTkr5rU1mdgLQ2t2nJ/hyvUMCgn5fIsC9b3zIQ/9ZyuATD+G/T1UUokg2eGb2Cl6cv4pf9eyoKVuSs1LawGVmbYC/Aj+rY8k8d/86ejvhkICgjX37E/7y7w+5oOgAbv6pohBFssGnFZv47b8iU7Yu73Zo0OWINJp6z4zNrDnwJHCTuy+rY1mohwRMKvuUP7ywkNOP3pc7zuusRiySBTRlS/JJKmfGg4BjgZvN7GbgTaCZuw+PWxPaIQGvLFjFsH8qClEk28SmbN1zfqGmbEnOq7cZu/t9wH31rAnlkIC3P/yC/3lMUYgi2UZTtiTf5Oxs6neXr2XIhDJFIYpkmS1V1fxKU7Ykz+Rkh1q0upJLHiylYA9FIYpkmztfWcSHmrIleSbnzoxjUYi7NW/CREUhimSVqR+WM+4dTdmS/JNTzXi7KMRBJdr0IZJFNGVL8lnOXKaORSGu3biVRy/rSoe2ikIUyRbxU7bGDjheU7Yk7+TEmXF8FOKYAcdTeKCiEEWyiaZsSb7L+mYcH4U46hfHKgpRJMt8tnYTv9OULclzWd2Mv6mu4erHIlGId5/fWVGIIlmmusa5dtJcHMiGs7sAAAcpSURBVE3ZkvyWta8Zx6IQX1/4ObecrShEkWw0esoSSj/RlC2RrDwzjkUhPj17Bdf27MiAHx0cdEkisoM0ZUtkm6xsxrEoxEEnHsLVikIUyTqasiWyvay7TB2LQjz/uAMYrihEkaykKVsi28uqM+Mno1GIvY7alzvO02/TItlIU7ZEvitrmvErC1YzNBqF+OeLutC0SdaULiJRsSlbh2nKlsh2sqKjRaIQZ1N44F7c31dRiCLZKH7K1p/6dNGULZE4oW/G8VGID11STItdsu5lbhFBU7ZEkgl1M160upKB42ZGohAvVRSiSCaZWVszm52O+9KULZHk6m3GZranmb1sZq+Z2TNmlnDro5mNNbNpZjY8HYUt+zIShbhrs+9FohBbKQpRJMPuBnbb2TvRlC2R+qVyZnwxMNLdTwNWA71qLzCz84Am7n4C0N7MOuxMUZ9XRqIQq6prmKAoRJGMM7NTgY1Ejvmd8sDUyJSt3599lI5lkTrU24zdfZS7vx79tABYk2BZd2BS9PZrwIm1F5jZEDMrM7Oy8vLypN9z9+ZN6LjPHowfWExHRSGKZFT06tdvgGFJ1qR8PO/balfOP+4AfqYpWyJ1Svk1YzM7AWjt7tMTfLkFsCJ6uwL4TmKDu4929yJ3LyooSP7ewj12bcbYSxSFKBKQYcAod19X14IdOZ57H7M//3t+oeYCiCSRUjM2szbAX4FL61iygW2vLbVM9X5FJJR6AFeZ2WSgi5mNCbgekZxX7/uEopesngRucvdldSybReTS9HSgEFictgpFJKPcvVvstplNdvfBQdYjkg9SedPuIOBY4GYzuxl4E2jm7vG7pp8FpppZO+B0oGvaKxWRjHP37kHXIJIP6m3G7n4fcF89ayrNrDvQE7jL3denpzwREZHcl7ZxVu6+lm07qkVERCRF2mglIiISMDVjERGRgKkZi4iIBMzcPfPf1KwcqOttUvH2Br5o5HIag+rOrFyu+wfunnyqRsBSPJ5z+TkKo2ytG7K39p06ngNpxqkyszJ3Lwq6jh2lujNLdYdftv6/qu7My9bad7ZuXaYWEREJmJqxiIhIwMLejEcHXUADqe7MUt3hl63/r6o787K19p2qO9SvGYuIiOSDsJ8Zi4iI5Dw1Y5FGZmZtzKynme0ddC2SOj1vkkmhaMZm1tbMpib5ejMze97M3jGzujKVMy6Fuvc3s8/MbHL0I/D3i5rZnmb2spm9ZmbPRCMyE60ba2bTzGx4oq9nWip1m1lTM1se93h3CqLWWjW1Bl4AioE36/o3ELbHO11S/fcWNqk+b2EV/dk0O+g6UhXGY3dHmdkoMzuroX8/8GYc/Uc/HmiRZNnVwCx3/zHwczPbIyPFJZFi3SXACHfvHv0oz0x1SV0MjHT304DVQK/aC8zsPKCJu58AtDezDhmuMZF66wY6A4/FPd7zM1phYp2Ba919BPAqkTjS7YT08U6XVJ63MKr3eQu5u4Hdgi5iB4Tx2E2ZmZ0E7Ovuzzf0PgJvxkA10AeoTLKmO9sSoaYAYXhDeCp1dwUGm9m7ZnZ7ZspKzt1Hufvr0U8LgDUJlnVn2+P9GnBiBkpLKsW6uwJnmllp9EwzbalkDeXub7n7dDPrRuQsa1qCZd0J2eOdLik+b6GT4vMWSmZ2KrCRyC8/2SJ0x26qzKwZ8ACw1MzOaej9BN6M3b0yhfzjFsCK6O0KoG3jVlW/FOt+mcgP2uOBE8ysc6MXliIzOwFo7e7TE3w5dI93TD11zwR6uHsx0Aw4I6PF1cHMjMgvbmuBqgRLQvt4p0s9z1sopfC8hU70ZYDfAMOCrmUHhfLYTVF/YCFwF1BsZlc35E4Cb8Yp2sC2Sy4tyZ66/+PuX7l7NTAbCMXlRzNrA/wVqOv191A+3inUPc/dV0VvlxGSx9sjrgLmAWcnWBLKxztdUnjeQimF5y2MhgGj3H1d0IXsoFAeuyk6Bhjt7quBicApDbmTbDnoZ7Ht0l0hsDS4UnbIq2a2n5ntDpwGLAi6oOhvzk8CN7l7XcP9Q/d4p1j3BDMrNLMmQG9gbsYKrIOZDTWz/tFP9wIS/ZAM3eOdLik+b6GT4vMWRj2Aq8xsMtDFzMYEXE+qQnfs7oCPgPbR20WkFoL0HaEZ+mFmk929e/T1jiPd/W9xX/sB8BLwBvAjoGv0bDNw9dR9CnAfsJXIb05/q+t+MsXMrgRuZ9s/9jeBZu4+PG5NK2Aq8G/gdCKPd32X5BtVinUfDTwKGPCcu9+c8UJriW70mwTsQuSXsb8DF4X98U6XBM/bfe7+RIAlpSTB83aVh+WHZYpiP5uCriMVYTx2UxXdUPwgkZeXmgE/d/cVyf9WgvvJln9fZtaOyNnDq7nygyrMoj+MegJTopdfpBHp8RbJb1nTjEVERHJVtrxmLCIikrPUjEVERAKmZiwiIhIwNWMREZGAqRmLiIgE7P8DmEKC5xxdNAkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (8, 4) # 设置画布大小\n",
    "\n",
    "plt.figure(1)  # 第一张画布\n",
    "ax1 = plt.subplot(121)  # 第一张画布的第一个子图\n",
    "plt.plot([1, 2, 3], [2, 4, 6])  # 这里的plt可以换成ax1\n",
    "\n",
    "ax2 = plt.subplot(122)  # 第一张画布的第二个子图\n",
    "plt.plot([2, 4, 6], [4, 8, 10])\n",
    "\n",
    "plt.figure(2)  # 第二张画布\n",
    "plt.plot([1, 2, 3], [4, 5, 6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在使用subplot()函数的时候，每次在新的子图上画图时，都得调用subplot()函数，例如第四个子图就得写成ax4 = plt.subplot(224)，那有没有什么办法，一次性就生成多个子图呢？这时候就可以用到subplots()函数，代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3., 0., 0., 0., 0., 1., 0., 0., 0., 1.]),\n",
       " array([2. , 2.2, 2.4, 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(10, 8)) \n",
    "ax1, ax2, ax3, ax4 = axes.flatten()\n",
    "ax1.plot([1, 2, 3], [2, 4, 6])  # 绘制第一个子图\n",
    "ax2.bar([1, 2, 3], [2, 4, 6])  # 绘制第二个子图\n",
    "ax3.scatter([1, 3, 5], [2, 4, 6])  # 绘制第三个子图\n",
    "ax4.hist([2, 2, 2, 3, 4])  # 绘制第四个子图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此外，如果要在subplot()函数或者subplots()函数生成的子图中设置子图标题、X轴标签或Y轴标签，得通过set_title()函数、set_xlabel()函数、set_ylabel()函数进行设置，演示代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3., 0., 0., 0., 0., 1., 0., 0., 0., 1.]),\n",
       " array([2. , 2.2, 2.4, 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "fig, axes = plt.subplots(2, 2, figsize=(10, 8)) \n",
    "ax1, ax2, ax3, ax4 = axes.flatten()\n",
    "ax1.plot([1, 2, 3], [2, 4, 6])  # 绘制第一个子图\n",
    "ax1.set_title('子图1')\n",
    "ax1.set_xlabel('日期')\n",
    "ax1.set_ylabel('分数')\n",
    "ax2.bar([1, 2, 3], [2, 4, 6])  # 绘制第二个子图\n",
    "ax3.scatter([1, 3, 5], [2, 4, 6])  # 绘制第三个子图\n",
    "ax4.hist([2, 2, 2, 3, 4])  # 绘制第四个子图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
